{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import time\n",
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pylab as plt\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import scorecardpy as sc\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from collections import Counter\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "try:\n",
    "    import cPickle as pickle\n",
    "except BaseException:\n",
    "    import pickle\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def psi_stats_score(data_left, data_right, non_computed=None, plot_image=True):\n",
    "    \"\"\"Calculate Average PSI value.\n",
    "    Parameters\n",
    "    ----------\n",
    "    data_left: pandas.DataFrame\n",
    "        The necessary columns score, y, id must be in data.\n",
    "\n",
    "    data_right: pandas.DataFrame\n",
    "        The necessary columns score, y, id must be in data.\n",
    "\n",
    "    non_computed : str or None\n",
    "        The column name of non-computed scoring indicators.\n",
    "        'True' means score by non_computed.\n",
    "        'False' means score by computed.\n",
    "\n",
    "    plot_image : bool (default True)\n",
    "        Plot image.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    psi_table : pandas.DataFrame\n",
    "        The PSI value of score interval.\n",
    "    \"\"\"\n",
    "\n",
    "    \"\"\"Check columns of data.\"\"\"\n",
    "    check_cols = ['score', 'y', 'id']\n",
    "    if non_computed != None and type(non_computed) == str:\n",
    "        check_cols += [non_computed]\n",
    "        data_left = data_left[~data_left[non_computed] == True].copy()\n",
    "        data_right = data_right[~data_right[non_computed] == True].copy()\n",
    "    elif non_computed == None:\n",
    "        pass\n",
    "    else:\n",
    "        raise ValueError('non_computed must be a str.')\n",
    "    for col in check_cols:\n",
    "        if col not in data_left.columns or col not in data_right.columns:\n",
    "            raise ValueError('Please check the columns %s of data' % col)\n",
    "\n",
    "    \"\"\"Drop NaN by column 'score'.\"\"\"\n",
    "    data_left = data_left.loc[data_left['score'].notnull(), check_cols]\n",
    "    data_right = data_right.loc[data_right['score'].notnull(), check_cols]\n",
    "\n",
    "    \"\"\"Discrete score value.\"\"\"\n",
    "    break_points = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
    "    data_left.loc[data_left.score < 0, 'score'] = 0\n",
    "    data_left.loc[data_left.score >= 100, 'score'] = 99\n",
    "    data_right.loc[data_right.score < 0, 'score'] = 0\n",
    "    data_right.loc[data_right.score >= 100, 'score'] = 99\n",
    "    data_left['score'] = pd.cut(data_left['score'], break_points, right=False).values\n",
    "    data_right['score'] = pd.cut(data_right['score'], break_points, right=False).values\n",
    "\n",
    "    \"\"\"Count psi of bad & good sample.\"\"\"\n",
    "    count_left = data_left.groupby(['score', 'y']).count()['id'].unstack().fillna(value=0.0)\n",
    "    count_right = data_right.groupby(['score', 'y']).count()['id'].unstack().fillna(value=0.0)\n",
    "    count_left['bad_ratio'] = count_left[1] / count_left[1].sum()\n",
    "    count_right['bad_ratio'] = count_right[1] / count_right[1].sum()\n",
    "    count_left['good_ratio'] = count_left[0] / count_left[0].sum()\n",
    "    count_right['good_ratio'] = count_right[0] / count_right[0].sum()\n",
    "    count_final = pd.merge(count_left, count_right, left_index=True,\n",
    "                           right_index=True, suffixes=['_left', '_right'])\n",
    "    count_final['psi_bad'] = (count_left['bad_ratio'] - count_right['bad_ratio']) * \\\n",
    "                             np.log(count_left['bad_ratio'] / count_right['bad_ratio'])\n",
    "    count_final['psi_good'] = (count_left['good_ratio'] - count_right['good_ratio']) * \\\n",
    "                              np.log(count_left['good_ratio'] / count_right['good_ratio'])\n",
    "\n",
    "    average_psi = (count_final['psi_bad'].replace([np.inf, np.nan], 0.0).sum() + \\\n",
    "                   count_final['psi_good'].replace([np.inf, np.nan], 0.0).sum()) / 2\n",
    "\n",
    "    \"\"\"Plot image\"\"\"\n",
    "    if plot_image == True:\n",
    "        plot_range = ['bad_ratio_left', 'good_ratio_left',\n",
    "                      'bad_ratio_right', 'good_ratio_right']\n",
    "        plot_label = ['Bad Ratio of Test Sample', 'Good Ratio of Test Sample',\n",
    "                      'Bad Ratio of Train Set', 'Good Ratio of Train Set']\n",
    "        color = ['blue', 'red', 'green', 'cyan']\n",
    "        marker = ['s', 'x', 'o', 'v']\n",
    "        plt.figure()\n",
    "        for p, l, c, m in zip(plot_range, plot_label, color, marker):\n",
    "            value = count_final[p].values\n",
    "            score_range = range(len(count_final.index))\n",
    "            score_label = ['[0,10)', '[10,20)',\n",
    "                           '[20,30)', '[30,40)',\n",
    "                           '[40,50]', '[50,60)',\n",
    "                           '[60,70)', '[70,80]',\n",
    "                           '[80,90]', '[90,100)']\n",
    "            plt.plot(score_range, value, color=c, marker=m,\n",
    "                     markersize=1, label=l)\n",
    "        plt.grid()\n",
    "        plt.legend(loc='upper left')\n",
    "        plt.xticks(score_range, score_label, rotation=45)\n",
    "        plt.title('PSI of Score Card')\n",
    "        plt.ylabel('Ratio')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    print('Average PSI:%f' % average_psi)\n",
    "    return count_final\n",
    "\n",
    "#定义KS计算函数\n",
    "from sklearn.metrics import roc_curve\n",
    "def calKS(y,y_pred):\n",
    "    fpr, tpr, threshold = metrics.roc_curve(y, y_pred)\n",
    "    return max(tpr - fpr)\n",
    "\n",
    "def psi_stats_score1(data_left, data_right, non_computed=None, plot_image=True):\n",
    "    \"\"\"Calculate Average PSI value.\n",
    "    Parameters\n",
    "    ----------\n",
    "    data_left: pandas.DataFrame\n",
    "        The necessary columns score, y, id must be in data.\n",
    "\n",
    "    data_right: pandas.DataFrame\n",
    "        The necessary columns score, y, id must be in data.\n",
    "\n",
    "    non_computed : str or None\n",
    "        The column name of non-computed scoring indicators.\n",
    "        'True' means score by non_computed.\n",
    "        'False' means score by computed.\n",
    "\n",
    "    plot_image : bool (default True)\n",
    "        Plot image.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    psi_table : pandas.DataFrame\n",
    "        The PSI value of score interval.\n",
    "    \"\"\"\n",
    "\n",
    "    \"\"\"Check columns of data.\"\"\"\n",
    "    check_cols = ['score', 'y', 'id']\n",
    "    if non_computed != None and type(non_computed) == str:\n",
    "        check_cols += [non_computed]\n",
    "        data_left = data_left[~data_left[non_computed] == True].copy()\n",
    "        data_right = data_right[~data_right[non_computed] == True].copy()\n",
    "    elif non_computed == None:\n",
    "        pass\n",
    "    else:\n",
    "        raise ValueError('non_computed must be a str.')\n",
    "    for col in check_cols:\n",
    "        if col not in data_left.columns or col not in data_right.columns:\n",
    "            raise ValueError('Please check the columns %s of data' % col)\n",
    "\n",
    "    \"\"\"Drop NaN by column 'score'.\"\"\"\n",
    "    data_left = data_left.loc[data_left['score'].notnull(), check_cols]\n",
    "    data_right = data_right.loc[data_right['score'].notnull(), check_cols]\n",
    "\n",
    "    \"\"\"Discrete score value.\"\"\"\n",
    "    break_points = list(range(300,1001,50))\n",
    "    data_left.loc[data_left.score < 300, 'score'] = 300\n",
    "    data_left.loc[data_left.score >= 1000, 'score'] = 999\n",
    "    data_right.loc[data_right.score < 300, 'score'] = 300\n",
    "    data_right.loc[data_right.score >= 1000, 'score'] = 999\n",
    "    data_left['score'] = pd.cut(data_left['score'], break_points, right=False).values\n",
    "    data_right['score'] = pd.cut(data_right['score'], break_points, right=False).values\n",
    "\n",
    "    \"\"\"Count psi of bad & good sample.\"\"\"\n",
    "    count_left = data_left.groupby(['score', 'y']).count()['id'].unstack().fillna(value=0.0)\n",
    "    count_right = data_right.groupby(['score', 'y']).count()['id'].unstack().fillna(value=0.0)\n",
    "    count_left['bad_ratio'] = count_left[1] / count_left[1].sum()\n",
    "    count_right['bad_ratio'] = count_right[1] / count_right[1].sum()\n",
    "    count_left['good_ratio'] = count_left[0] / count_left[0].sum()\n",
    "    count_right['good_ratio'] = count_right[0] / count_right[0].sum()\n",
    "    count_final = pd.merge(count_left, count_right, left_index=True,\n",
    "                           right_index=True, suffixes=['_left', '_right'])\n",
    "    count_final['psi_bad'] = (count_left['bad_ratio'] - count_right['bad_ratio']) * \\\n",
    "                             np.log(count_left['bad_ratio'] / count_right['bad_ratio'])\n",
    "    count_final['psi_good'] = (count_left['good_ratio'] - count_right['good_ratio']) * \\\n",
    "                              np.log(count_left['good_ratio'] / count_right['good_ratio'])\n",
    "\n",
    "    average_psi = (count_final['psi_bad'].replace([np.inf, np.nan], 0.0).sum() + \\\n",
    "                   count_final['psi_good'].replace([np.inf, np.nan], 0.0).sum()) / 2\n",
    "\n",
    "    \"\"\"Plot image\"\"\"\n",
    "    if plot_image == True:\n",
    "        plot_range = ['bad_ratio_left', 'good_ratio_left',\n",
    "                      'bad_ratio_right', 'good_ratio_right']\n",
    "        plot_label = ['Bad Ratio of Test Sample', 'Good Ratio of Test Sample',\n",
    "                      'Bad Ratio of Train Set', 'Good Ratio of Train Set']\n",
    "        color = ['blue', 'red', 'green', 'cyan']\n",
    "        marker = ['s', 'x', 'o', 'v']\n",
    "        plt.figure(figsize=(9,6))\n",
    "        for p, l, c, m in zip(plot_range, plot_label, color, marker):\n",
    "            value = count_final[p].values\n",
    "            score_range = range(len(count_final.index))\n",
    "            score_label = ['[300,350)', '[350,400)',\n",
    "                           '[400,450)', '[450,500)',\n",
    "                           '[500,550]', '[550,600)',\n",
    "                           '[600,650)', '[650,700]',\n",
    "                           '[700,750]', '[750,800)',\n",
    "                           '[800,850]', '[850,900)',\n",
    "                           '[900,950]', '[950,1000)']\n",
    "            plt.plot(score_range, value, color=c, marker=m,\n",
    "                     markersize=1, label=l)\n",
    "        plt.grid()\n",
    "        plt.legend(loc='upper left')\n",
    "        plt.xticks(score_range, score_label, rotation=45)\n",
    "        plt.title('PSI of Score Card')\n",
    "        plt.ylabel('Ratio')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    print('Average PSI:%f' % average_psi)\n",
    "    return count_final\n",
    "\n",
    "#类别变量转数值变量\n",
    "def cate_var_transform(X, Y):\n",
    "    ##取出数据类型\n",
    "    d_type = X.dtypes\n",
    "    object_var = X.iloc[:, np.where(d_type == \"object\")[0]]\n",
    "    num_var = X.iloc[:, np.where(d_type != \"object\")[0]]\n",
    "\n",
    "    # object_transfer_rule用于记录每个类别变量的数值转换规则\n",
    "    object_transfer_rule = list(np.zeros([len(object_var.columns)]))\n",
    "\n",
    "    # object_transform是类别变量数值化转化后的值\n",
    "    object_transform = pd.DataFrame(np.zeros(object_var.shape),\n",
    "                                    columns=object_var.columns)\n",
    "\n",
    "    for i in range(0, len(object_var.columns)):\n",
    "\n",
    "        temp_var = object_var.iloc[:, i]\n",
    "\n",
    "        ##除空值外的取值种类\n",
    "        unique_value = np.unique(temp_var.iloc[np.where(~temp_var.isna())[0]])\n",
    "\n",
    "        transform_rule = pd.concat([pd.DataFrame(unique_value, columns=['raw data']),\n",
    "                                    pd.DataFrame(np.zeros([len(unique_value), 2]),\n",
    "                                                 columns=['transform data', 'bad rate'])], axis=1)\n",
    "        for j in range(0, len(unique_value)):\n",
    "            bad_num = len(np.where((Y == 1) & (temp_var == unique_value[j]))[0])\n",
    "            all_num = len(np.where(temp_var == unique_value[j])[0])\n",
    "\n",
    "            # 计算badprob\n",
    "            if all_num == 0:  # 防止all_num=0的情况，报错\n",
    "                all_num = 0.5\n",
    "            transform_rule.iloc[j, 2] = 1.0000000 * bad_num / all_num\n",
    "\n",
    "        # 按照badprob排序，给出转换后的数值\n",
    "        transform_rule = transform_rule.sort_values(by='bad rate')\n",
    "        transform_rule.iloc[:, 1] = list(range(len(unique_value), 0, -1))\n",
    "\n",
    "        # 保存转换规则\n",
    "        object_transfer_rule[i] = {object_var.columns[i]: transform_rule}\n",
    "        # 转换变量\n",
    "        for k in range(0, len(unique_value)):\n",
    "            transfer_value = transform_rule.iloc[np.where(transform_rule.iloc[:, 0] == unique_value[k])[0], 1]\n",
    "            object_transform.iloc[np.where(temp_var == unique_value[k])[0], i] = float(transfer_value)\n",
    "        object_transform.iloc[np.where(object_transform.iloc[:, i] == 0)[0], i] = np.nan\n",
    "\n",
    "    X_transformed = pd.concat([num_var, object_transform], axis=1)\n",
    "    return (X_transformed, transform_rule)\n",
    "\n",
    "#去除缺失率、单一值率过高的变量\n",
    "def missing_identity_select(data,y='flagy', missing_rate=0.9, identity_rate=0.9, kp_vars=None):\n",
    "    if kp_vars:\n",
    "        data1 = data.drop(columns=kp_vars)\n",
    "    else:\n",
    "        data1 = data.copy()\n",
    "    null_ratio = data1.isnull().sum() / data1.shape[0]\n",
    "    data1 = data1.iloc[:,np.where(null_ratio <= missing_rate)[0]]\n",
    "    identity = data1.drop(columns='flagy').apply(lambda x: x.value_counts().max() / x.size).reset_index(name='identity_rate').rename(columns={'index': 'variable'})\n",
    "    identity_vars = identity[identity['identity_rate'] <= identity_rate]['variable'].to_list()\n",
    "    data1 = data[identity_vars + kp_vars + ['flagy']]\n",
    "    return data1\n",
    "#剔除自变量与因变量相关性过低、自变量与自变量相关性高的变量\n",
    "def delete_corelation(data, y='flagy', y_cor=0.1, x_cor=0.7, kp_vars=None):\n",
    "    if kp_vars:\n",
    "        data1 = data.drop(columns=kp_vars) \n",
    "    cor = data1.corr().abs()\n",
    "    cor_y = cor[y]\n",
    "    y_select = cor_y[cor_y >= y_cor ]\n",
    "    y_select.sort_values(ascending=False)\n",
    "    cor_x = cor.loc[y_select.index.values, y_select.index.values]\n",
    "    cor_x.drop(labels=y, inplace=True)\n",
    "    cor_x.drop(columns=y, inplace=True)\n",
    "    drop_index = []\n",
    "    for i in range(len(cor_x)-1):\n",
    "        drop_index.extend(np.where(cor_x.iloc[i+1:,i] >= x_cor)[0])\n",
    "    drop_index = list(set(drop_index))\n",
    "    drop_name = cor_x.index[drop_index].tolist()\n",
    "    cor_x.drop(index=drop_name, inplace=True)\n",
    "    final_vars = cor_x.index.tolist() + kp_vars + [y]\n",
    "    data = data[final_vars]\n",
    "    return data\n",
    "#变量PSI计算\n",
    "def psi_var(data_train, data_test):\n",
    "    data_train1 = data_train.copy()\n",
    "    data_test1 = data_test.copy()\n",
    "    var_names = data_train.columns.tolist()\n",
    "    drop = []\n",
    "    for name in var_names:\n",
    "        num = data_train[name].unique().shape[0]\n",
    "        if num <= 6:\n",
    "            breaks = np.linspace(data_train[name].min()-1, data_train[name].max(), num+1)\n",
    "        else:\n",
    "            breaks = np.linspace(data_train[name].min()-1, data_train[name].max(), 6+1)\n",
    "        data_train1[name] = pd.cut(data_train[name], bins=breaks, right=True)\n",
    "        data_test1[name] = pd.cut(data_test[name], bins=breaks, right=True)\n",
    "        data_train1[name] = data_train1[name].astype(str)\n",
    "        data_test1[name] = data_test1[name].astype(str)\n",
    "        df1 = data_train1[name].value_counts().rename('train')\n",
    "        df2 = data_test1[name].value_counts().rename('test')\n",
    "        df = pd.concat([df1, df2], axis=1)\n",
    "        df['train_ratio'] = df.train / df.train.sum()\n",
    "        df['test_ratio'] = df.test / df.test.sum()\n",
    "        df['psi'] = (df.train_ratio - df.test_ratio) * np.log(df.train_ratio / df.test_ratio)\n",
    "        df.psi.replace(np.inf, 0)\n",
    "        if df.psi.sum() > 0.1:\n",
    "            #del data_train[name]\n",
    "            #del data_test[name]\n",
    "            drop.append(name)\n",
    "        del df1\n",
    "        del df2\n",
    "        del df\n",
    "    print('PSI偏高的被剔除的变量：\\n{}'.format(drop))\n",
    "    return drop\n",
    "\n",
    "def psi_var_table(data_train, data_test):\n",
    "    psi_table = pd.DataFrame()\n",
    "    data_train1 = data_train.copy()\n",
    "    data_test1 = data_test.copy()\n",
    "    var_names = data_train.columns.tolist()\n",
    "    drop = []\n",
    "    for name in var_names:\n",
    "        num = data_train[name].unique().shape[0]\n",
    "        if num <= 6:\n",
    "            breaks = data_train[name].value_counts().sort_index().index.to_list()\n",
    "            #breaks = np.linspace(data_train[name].min()-1, data_train[name].max(), num+1)\n",
    "        else:\n",
    "            breaks = np.linspace(data_train[name].min(), data_train[name].max(), 6)\n",
    "        \n",
    "        breaks = [-np.inf] + list(breaks) + [np.inf]\n",
    "        data_train1[name] = pd.cut(data_train[name], bins=breaks, right=True)\n",
    "        data_test1[name] = pd.cut(data_test[name], bins=breaks, right=True)\n",
    "        data_train1[name] = data_train1[name].astype(str)\n",
    "        data_test1[name] = data_test1[name].astype(str)\n",
    "        df1 = data_train1[name].value_counts().rename('train')\n",
    "        df2 = data_test1[name].value_counts().rename('test')\n",
    "        df = pd.concat([df1, df2], axis=1)\n",
    "        df['train_ratio'] = df.train / df.train.sum()\n",
    "        df['test_ratio'] = df.test / df.test.sum()\n",
    "        df['psi'] = (df.train_ratio - df.test_ratio) * np.log(df.train_ratio / df.test_ratio)\n",
    "        #df.psi.replace(np.inf, 0)\n",
    "        df['psi_all'] = df.psi.sum()\n",
    "        df['var'] = name\n",
    "        df.reset_index(inplace=True)\n",
    "        psi_table = pd.concat([psi_table, df],axis=0, sort=False)\n",
    "        psi_table = psi_table[['var', 'index','train', 'train_ratio', 'test', 'test_ratio', 'psi', 'psi_all']]\n",
    "    return psi_table\n",
    "\n",
    "def badrate_month(df):\n",
    "    df['user_date'] = pd.to_datetime(df['user_date'])\n",
    "    df_temp = df[['user_date','flagy']]\n",
    "    df_temp['year'] = df_temp['user_date'].apply(lambda x : str(x.year))\n",
    "    df_temp['month'] = df_temp['user_date'].apply(lambda x : str(x.month)+'月')\n",
    "    badrate_m = df_temp.groupby(['year','month'])['flagy'].agg([len,sum])\n",
    "    badrate_m['坏客率%'] = badrate_m['sum']/badrate_m['len']*100 \n",
    "    badrate_m.rename(columns = {'len':'样本量','sum':'坏客数量'},inplace = True)\n",
    "    return badrate_m\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## y标签自定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('dts_result_y.csv',index_col = 'cus_num',low_memory = False,encoding = 'utf-8',skiprows = [1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['als_m1_id_bank_selfnum',\n",
       " 'als_m1_id_nbank_selfnum',\n",
       " 'als_m1_cell_bank_selfnum',\n",
       " 'als_m1_cell_nbank_selfnum']"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1_selfnum = [i for i in df.columns if re.search(r'(_m1_).*?(selfnum)',i) != None]\n",
    "m1_selfnum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.118"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "criteria = 'als_m1_id_bank_selfnum > 0 | als_m1_cell_bank_selfnum > 0'\n",
    "df.query(criteria).shape[0]/5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df.loc[df.eval(criteria), 'other_var1'] = 1 \n",
    "df = df.iloc[:,:5]\n",
    "df.rename(columns = {'other_var1':'flagy'},inplace = True)\n",
    "df.to_csv('自定义标签.csv',encoding = 'utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 拼表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\项目\\\\8.11_建模_借转贷'"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd() # 查看当前所在位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "os.chdir('D:\\\\项目\\\\8.11_建模_借转贷')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.ipynb_checkpoints',\n",
       " 'dts_result_x.csv',\n",
       " 'dts_result_y.csv',\n",
       " 'lgb.ipynb',\n",
       " 'sha256_dts_5000_x.xlsx',\n",
       " 'sha256_dts_5000_y.xlsx',\n",
       " '~$产品文档.xlsx',\n",
       " '产品文档.xlsx',\n",
       " '原始样本&标签',\n",
       " '自定义标签.csv']"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>张##</td>\n",
       "      <td>2021-04-24</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>023DBFCE637ED87F6401C527F807D0E0905B7AFC6EC71A...</td>\n",
       "      <td>4015EDD4EA09E6083D2D9791A206FD522B0F421C2417CE...</td>\n",
       "      <td>张##</td>\n",
       "      <td>2021-04-24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>13BF60EFBDCFDAB2B337CEC45BA3105E1C21CA56454F6A...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>F6E787ADE33B9C3D8E8C6474E51CA41BFC5A692DF0EA00...</td>\n",
       "      <td>7F953992D61B1C5A2D4B99A2AB847F42113C0350CCCC1F...</td>\n",
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       "    <tr>\n",
       "      <th>4997</th>\n",
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       "</table>\n",
       "<p>5000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      cell  \\\n",
       "cus_num                                                      \n",
       "4        01CEB2D50F2C09C2C68F9674EB08E2C7F0ED672293BEC2...   \n",
       "1        649BF8AD60FAC303FD94B7D41FB52E16A8E53DB1125AB3...   \n",
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       "5        F5FEB2AAB5A6294212C117CAF843432C1C21DB7D3E90A7...   \n",
       "...                                                    ...   \n",
       "4996     CD408DAC63FC04EDC2BDD1FDE86DBBCFFB70492C65239F...   \n",
       "4999     023DBFCE637ED87F6401C527F807D0E0905B7AFC6EC71A...   \n",
       "4998     13BF60EFBDCFDAB2B337CEC45BA3105E1C21CA56454F6A...   \n",
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       "4997     8ADB68A816B632E40D46F7427B7E6F795177F3C30FD6B2...   \n",
       "\n",
       "                                                        id name   user_date  \\\n",
       "cus_num                                                                       \n",
       "4        82529756181D23ED5F8603FF9B4DD30DAA8D3A6BA32848...  张##  2021-04-24   \n",
       "1        9F845C5F7EFC5B783BA399726B97654D832A2FA00849F7...  张##  2021-04-24   \n",
       "2        67B18EB2185CAC0792DF706CEAAFE93AE1187E6A483E60...  张##  2021-04-24   \n",
       "3        13C103356DC73C860FB5D009EF2C1947599094C565A4D9...  张##  2021-04-24   \n",
       "5        FDDB09AB4BFE0857627681B895C0AE99234758FBE97C62...  张##  2021-04-24   \n",
       "...                                                    ...  ...         ...   \n",
       "4996     3208FFF98F0E87A96065E67A2AC1EC1D03BDA256495DC5...  张##  2021-04-24   \n",
       "4999     4015EDD4EA09E6083D2D9791A206FD522B0F421C2417CE...  张##  2021-04-24   \n",
       "4998     83F27FE66FE6059ADC4D71990F7FC385F29EA57E87FB11...  张##  2021-04-24   \n",
       "5000     7F953992D61B1C5A2D4B99A2AB847F42113C0350CCCC1F...  张##  2021-04-24   \n",
       "4997     5282D48C8510BC7F910B630A763EB57C7094DA0CF73D75...  张##  2021-04-24   \n",
       "\n",
       "         flagy  \n",
       "cus_num         \n",
       "4            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "5            0  \n",
       "...        ...  \n",
       "4996         0  \n",
       "4999         0  \n",
       "4998         0  \n",
       "5000         0  \n",
       "4997         0  \n",
       "\n",
       "[5000 rows x 5 columns]"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_y = pd.read_csv('自定义标签.csv',index_col = 'cus_num',low_memory = False,header = 0,encoding = 'utf-8')\n",
    "df_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "      <th>other_var2</th>\n",
       "      <th>swift_number</th>\n",
       "      <th>cus_username</th>\n",
       "      <th>code</th>\n",
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       "      <th>alu_m12_cell_avg_monnum</th>\n",
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       "      <th>3</th>\n",
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       "      <td>13C103356DC73C860FB5D009EF2C1947599094C565A4D9...</td>\n",
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       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
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       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
       "      <td>100001</td>\n",
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       "      <td>张##</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
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       "      <td>4005527_20210811104507_52980782A</td>\n",
       "      <td>chang.lu_test</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>CD408DAC63FC04EDC2BDD1FDE86DBBCFFB70492C65239F...</td>\n",
       "      <td>3208FFF98F0E87A96065E67A2AC1EC1D03BDA256495DC5...</td>\n",
       "      <td>张##</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
       "      <td>100001</td>\n",
       "      <td>0</td>\n",
       "      <td>4005527_20210811104744_606905A9A</td>\n",
       "      <td>chang.lu_test</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>13BF60EFBDCFDAB2B337CEC45BA3105E1C21CA56454F6A...</td>\n",
       "      <td>83F27FE66FE6059ADC4D71990F7FC385F29EA57E87FB11...</td>\n",
       "      <td>张##</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
       "      <td>100001</td>\n",
       "      <td>0</td>\n",
       "      <td>4005527_20210811104745_22230420A</td>\n",
       "      <td>chang.lu_test</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>023DBFCE637ED87F6401C527F807D0E0905B7AFC6EC71A...</td>\n",
       "      <td>4015EDD4EA09E6083D2D9791A206FD522B0F421C2417CE...</td>\n",
       "      <td>张##</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
       "      <td>100001</td>\n",
       "      <td>0</td>\n",
       "      <td>4005527_20210811104745_22240420A</td>\n",
       "      <td>chang.lu_test</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>F6E787ADE33B9C3D8E8C6474E51CA41BFC5A692DF0EA00...</td>\n",
       "      <td>7F953992D61B1C5A2D4B99A2AB847F42113C0350CCCC1F...</td>\n",
       "      <td>张##</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
       "      <td>100001</td>\n",
       "      <td>0</td>\n",
       "      <td>4005527_20210811104745_609606B3A</td>\n",
       "      <td>chang.lu_test</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>8ADB68A816B632E40D46F7427B7E6F795177F3C30FD6B2...</td>\n",
       "      <td>5282D48C8510BC7F910B630A763EB57C7094DA0CF73D75...</td>\n",
       "      <td>张##</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
       "      <td>100001</td>\n",
       "      <td>0</td>\n",
       "      <td>4005527_20210811104745_609506B3A</td>\n",
       "      <td>chang.lu_test</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 3820 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      cell  \\\n",
       "cus_num                                                      \n",
       "4        01CEB2D50F2C09C2C68F9674EB08E2C7F0ED672293BEC2...   \n",
       "2        4224F1B85C4F00B65D2DEF3424728B9F271E446A4DBD7F...   \n",
       "3        73C5FA2264A2A574CC8AD15A9CAA021DDF6C1F60B364AD...   \n",
       "1        649BF8AD60FAC303FD94B7D41FB52E16A8E53DB1125AB3...   \n",
       "5        F5FEB2AAB5A6294212C117CAF843432C1C21DB7D3E90A7...   \n",
       "...                                                    ...   \n",
       "4996     CD408DAC63FC04EDC2BDD1FDE86DBBCFFB70492C65239F...   \n",
       "4998     13BF60EFBDCFDAB2B337CEC45BA3105E1C21CA56454F6A...   \n",
       "4999     023DBFCE637ED87F6401C527F807D0E0905B7AFC6EC71A...   \n",
       "5000     F6E787ADE33B9C3D8E8C6474E51CA41BFC5A692DF0EA00...   \n",
       "4997     8ADB68A816B632E40D46F7427B7E6F795177F3C30FD6B2...   \n",
       "\n",
       "                                                        id name   user_date  \\\n",
       "cus_num                                                                       \n",
       "4        82529756181D23ED5F8603FF9B4DD30DAA8D3A6BA32848...  张##  2021-03-25   \n",
       "2        67B18EB2185CAC0792DF706CEAAFE93AE1187E6A483E60...  张##  2021-03-25   \n",
       "3        13C103356DC73C860FB5D009EF2C1947599094C565A4D9...  张##  2021-03-25   \n",
       "1        9F845C5F7EFC5B783BA399726B97654D832A2FA00849F7...  张##  2021-03-25   \n",
       "5        FDDB09AB4BFE0857627681B895C0AE99234758FBE97C62...  张##  2021-03-25   \n",
       "...                                                    ...  ...         ...   \n",
       "4996     3208FFF98F0E87A96065E67A2AC1EC1D03BDA256495DC5...  张##  2021-03-25   \n",
       "4998     83F27FE66FE6059ADC4D71990F7FC385F29EA57E87FB11...  张##  2021-03-25   \n",
       "4999     4015EDD4EA09E6083D2D9791A206FD522B0F421C2417CE...  张##  2021-03-25   \n",
       "5000     7F953992D61B1C5A2D4B99A2AB847F42113C0350CCCC1F...  张##  2021-03-25   \n",
       "4997     5282D48C8510BC7F910B630A763EB57C7094DA0CF73D75...  张##  2021-03-25   \n",
       "\n",
       "        other_var1  custApiCode  other_var2                      swift_number  \\\n",
       "cus_num                                                                         \n",
       "4            jzdcs       100001           0  4005527_20210811104507_08400420A   \n",
       "2            jzdcs       100001           0  4005527_20210811104507_468805A9A   \n",
       "3            jzdcs       100001           0  4005527_20210811104507_08410420A   \n",
       "1            jzdcs       100001           1  4005527_20210811104507_52990782A   \n",
       "5            jzdcs       100001           0  4005527_20210811104507_52980782A   \n",
       "...            ...          ...         ...                               ...   \n",
       "4996         jzdcs       100001           0  4005527_20210811104744_606905A9A   \n",
       "4998         jzdcs       100001           0  4005527_20210811104745_22230420A   \n",
       "4999         jzdcs       100001           0  4005527_20210811104745_22240420A   \n",
       "5000         jzdcs       100001           0  4005527_20210811104745_609606B3A   \n",
       "4997         jzdcs       100001           0  4005527_20210811104745_609506B3A   \n",
       "\n",
       "          cus_username  code  ...  alu_m12_cell_avg_monnum  \\\n",
       "cus_num                       ...                            \n",
       "4        chang.lu_test     0  ...                      NaN   \n",
       "2        chang.lu_test     0  ...                      NaN   \n",
       "3        chang.lu_test     0  ...                      NaN   \n",
       "1        chang.lu_test     0  ...                      NaN   \n",
       "5        chang.lu_test     0  ...                      NaN   \n",
       "...                ...   ...  ...                      ...   \n",
       "4996     chang.lu_test     0  ...                      NaN   \n",
       "4998     chang.lu_test     0  ...                      NaN   \n",
       "4999     chang.lu_test     0  ...                      NaN   \n",
       "5000     chang.lu_test     0  ...                      NaN   \n",
       "4997     chang.lu_test     0  ...                      NaN   \n",
       "\n",
       "         alu_m12_cell_max_monnum  alu_m12_cell_min_monnum  alu_fst_id_inteday  \\\n",
       "cus_num                                                                         \n",
       "4                            NaN                      NaN                 NaN   \n",
       "2                            NaN                      NaN                 NaN   \n",
       "3                            NaN                      NaN                 NaN   \n",
       "1                            NaN                      NaN                 NaN   \n",
       "5                            NaN                      NaN                 NaN   \n",
       "...                          ...                      ...                 ...   \n",
       "4996                         NaN                      NaN                 NaN   \n",
       "4998                         NaN                      NaN                 NaN   \n",
       "4999                         NaN                      NaN                 NaN   \n",
       "5000                         NaN                      NaN                 NaN   \n",
       "4997                         NaN                      NaN                 NaN   \n",
       "\n",
       "         alu_lst_id_inteday  alu_fst_cell_inteday  alu_lst_cell_inteday  \\\n",
       "cus_num                                                                   \n",
       "4                       NaN                   NaN                   NaN   \n",
       "2                       NaN                   NaN                   NaN   \n",
       "3                       NaN                   NaN                   NaN   \n",
       "1                       NaN                   NaN                   NaN   \n",
       "5                       NaN                   NaN                   NaN   \n",
       "...                     ...                   ...                   ...   \n",
       "4996                    NaN                   NaN                   NaN   \n",
       "4998                    NaN                   NaN                   NaN   \n",
       "4999                    NaN                   NaN                   NaN   \n",
       "5000                    NaN                   NaN                   NaN   \n",
       "4997                    NaN                   NaN                   NaN   \n",
       "\n",
       "         flag_fraudrelation_g  frg_list_level  frg_group_num  \n",
       "cus_num                                                       \n",
       "4                           0             NaN            NaN  \n",
       "2                           0             NaN            NaN  \n",
       "3                           0             NaN            NaN  \n",
       "1                           0             NaN            NaN  \n",
       "5                           0             NaN            NaN  \n",
       "...                       ...             ...            ...  \n",
       "4996                        0             NaN            NaN  \n",
       "4998                        0             NaN            NaN  \n",
       "4999                        0             NaN            NaN  \n",
       "5000                        0             NaN            NaN  \n",
       "4997                        0             NaN            NaN  \n",
       "\n",
       "[5000 rows x 3820 columns]"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x = pd.read_csv('dts_result_x.csv',index_col = 'cus_num',low_memory = False,header = 0,encoding = 'utf-8',skiprows = [1])\n",
    "df_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df = pd.concat([df_x,df_y['flagy']],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.8744\n",
       "1    0.1256\n",
       "Name: other_var2, dtype: float64"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.other_var2.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4518"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.flagy == df.other_var2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "368"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(df.query('flagy == 1').index)&set(df.query('other_var2 == 1').index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('建模样本&自定标签&之前标签.csv',encoding = 'utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取终版数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('建模样本&自定标签&之前标签.csv',index_col = 'cus_num',low_memory = False,encoding = 'utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>user_date</th>\n",
       "      <th>other_var1</th>\n",
       "      <th>custApiCode</th>\n",
       "      <th>other_var2</th>\n",
       "      <th>swift_number</th>\n",
       "      <th>cus_username</th>\n",
       "      <th>code</th>\n",
       "      <th>...</th>\n",
       "      <th>alu_m12_cell_max_monnum</th>\n",
       "      <th>alu_m12_cell_min_monnum</th>\n",
       "      <th>alu_fst_id_inteday</th>\n",
       "      <th>alu_lst_id_inteday</th>\n",
       "      <th>alu_fst_cell_inteday</th>\n",
       "      <th>alu_lst_cell_inteday</th>\n",
       "      <th>flag_fraudrelation_g</th>\n",
       "      <th>frg_list_level</th>\n",
       "      <th>frg_group_num</th>\n",
       "      <th>flagy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cus_num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>649BF8AD60FAC303FD94B7D41FB52E16A8E53DB1125AB3...</td>\n",
       "      <td>9F845C5F7EFC5B783BA399726B97654D832A2FA00849F7...</td>\n",
       "      <td>张##</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
       "      <td>100001</td>\n",
       "      <td>1</td>\n",
       "      <td>4005527_20210811104507_52990782A</td>\n",
       "      <td>chang.lu_test</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4224F1B85C4F00B65D2DEF3424728B9F271E446A4DBD7F...</td>\n",
       "      <td>67B18EB2185CAC0792DF706CEAAFE93AE1187E6A483E60...</td>\n",
       "      <td>张##</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
       "      <td>100001</td>\n",
       "      <td>0</td>\n",
       "      <td>4005527_20210811104507_468805A9A</td>\n",
       "      <td>chang.lu_test</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73C5FA2264A2A574CC8AD15A9CAA021DDF6C1F60B364AD...</td>\n",
       "      <td>13C103356DC73C860FB5D009EF2C1947599094C565A4D9...</td>\n",
       "      <td>张##</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
       "      <td>100001</td>\n",
       "      <td>0</td>\n",
       "      <td>4005527_20210811104507_08410420A</td>\n",
       "      <td>chang.lu_test</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01CEB2D50F2C09C2C68F9674EB08E2C7F0ED672293BEC2...</td>\n",
       "      <td>82529756181D23ED5F8603FF9B4DD30DAA8D3A6BA32848...</td>\n",
       "      <td>张##</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
       "      <td>100001</td>\n",
       "      <td>0</td>\n",
       "      <td>4005527_20210811104507_08400420A</td>\n",
       "      <td>chang.lu_test</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F5FEB2AAB5A6294212C117CAF843432C1C21DB7D3E90A7...</td>\n",
       "      <td>FDDB09AB4BFE0857627681B895C0AE99234758FBE97C62...</td>\n",
       "      <td>张##</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>jzdcs</td>\n",
       "      <td>100001</td>\n",
       "      <td>0</td>\n",
       "      <td>4005527_20210811104507_52980782A</td>\n",
       "      <td>chang.lu_test</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3821 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      cell  \\\n",
       "cus_num                                                      \n",
       "1        649BF8AD60FAC303FD94B7D41FB52E16A8E53DB1125AB3...   \n",
       "2        4224F1B85C4F00B65D2DEF3424728B9F271E446A4DBD7F...   \n",
       "3        73C5FA2264A2A574CC8AD15A9CAA021DDF6C1F60B364AD...   \n",
       "4        01CEB2D50F2C09C2C68F9674EB08E2C7F0ED672293BEC2...   \n",
       "5        F5FEB2AAB5A6294212C117CAF843432C1C21DB7D3E90A7...   \n",
       "\n",
       "                                                        id name   user_date  \\\n",
       "cus_num                                                                       \n",
       "1        9F845C5F7EFC5B783BA399726B97654D832A2FA00849F7...  张##  2021-03-25   \n",
       "2        67B18EB2185CAC0792DF706CEAAFE93AE1187E6A483E60...  张##  2021-03-25   \n",
       "3        13C103356DC73C860FB5D009EF2C1947599094C565A4D9...  张##  2021-03-25   \n",
       "4        82529756181D23ED5F8603FF9B4DD30DAA8D3A6BA32848...  张##  2021-03-25   \n",
       "5        FDDB09AB4BFE0857627681B895C0AE99234758FBE97C62...  张##  2021-03-25   \n",
       "\n",
       "        other_var1  custApiCode  other_var2                      swift_number  \\\n",
       "cus_num                                                                         \n",
       "1            jzdcs       100001           1  4005527_20210811104507_52990782A   \n",
       "2            jzdcs       100001           0  4005527_20210811104507_468805A9A   \n",
       "3            jzdcs       100001           0  4005527_20210811104507_08410420A   \n",
       "4            jzdcs       100001           0  4005527_20210811104507_08400420A   \n",
       "5            jzdcs       100001           0  4005527_20210811104507_52980782A   \n",
       "\n",
       "          cus_username  code  ...  alu_m12_cell_max_monnum  \\\n",
       "cus_num                       ...                            \n",
       "1        chang.lu_test     0  ...                      NaN   \n",
       "2        chang.lu_test     0  ...                      NaN   \n",
       "3        chang.lu_test     0  ...                      NaN   \n",
       "4        chang.lu_test     0  ...                      NaN   \n",
       "5        chang.lu_test     0  ...                      NaN   \n",
       "\n",
       "         alu_m12_cell_min_monnum  alu_fst_id_inteday  alu_lst_id_inteday  \\\n",
       "cus_num                                                                    \n",
       "1                            NaN                 NaN                 NaN   \n",
       "2                            NaN                 NaN                 NaN   \n",
       "3                            NaN                 NaN                 NaN   \n",
       "4                            NaN                 NaN                 NaN   \n",
       "5                            NaN                 NaN                 NaN   \n",
       "\n",
       "         alu_fst_cell_inteday  alu_lst_cell_inteday  flag_fraudrelation_g  \\\n",
       "cus_num                                                                     \n",
       "1                         NaN                   NaN                     0   \n",
       "2                         NaN                   NaN                     0   \n",
       "3                         NaN                   NaN                     0   \n",
       "4                         NaN                   NaN                     0   \n",
       "5                         NaN                   NaN                     0   \n",
       "\n",
       "         frg_list_level  frg_group_num  flagy  \n",
       "cus_num                                        \n",
       "1                   NaN            NaN      0  \n",
       "2                   NaN            NaN      0  \n",
       "3                   NaN            NaN      0  \n",
       "4                   NaN            NaN      0  \n",
       "5                   NaN            NaN      0  \n",
       "\n",
       "[5 rows x 3821 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.799999999999999%\n"
     ]
    }
   ],
   "source": [
    "# 坏客率\n",
    "badper = sum(df['flagy'] == 1)/len(df['flagy']) * 100\n",
    "print(f'{badper}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = ['name','id','cell','swift_number','cus_username','code'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 3815)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 变量粗筛"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 1037)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 2.1 缺失值、同一值过高\n",
    "data = missing_identity_select(df, 'flagy', missing_rate=0.9, identity_rate=0.9, kp_vars=['user_date']) \n",
    "# data = delete_corelation(data, 'flagy', y_cor=0.0, x_cor=0.95, kp_vars=['user_date'])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_vars = [\n",
    "    'als_m1_cell_rel_allnum', 'als_m1_id_rel_allnum', 'als_m1_id_rel_orgnum',\n",
    "    'user_date', 'als_m1_cell_rel_orgnum', 'ir_m1_id_x_cell_cnt',\n",
    "    'ir_m1_id_x_name_cnt', 'ir_m1_id_x_tel_home_cnt',\n",
    "    'ir_m1_cell_x_linkman_cell_cnt', 'ir_m1_id_x_mail_cnt',\n",
    "    'ir_m1_id_x_biz_addr_cnt','ir_m1_id_x_tel_biz_cnt'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_vars = ['other_var2', 'flag_inforelation','user_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_vars = ['flagy', 'flag_inforelation','user_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "Y = data['flagy']\n",
    "X = data.drop(columns=['flagy'])\n",
    "\n",
    "# X_transform, transfrom_rule = cate_var_transform(X, Y, kp_vars = ['user_date', 'id', 'cell'])\n",
    "data_train_x, data_test_x, data_train_y, data_test_y = train_test_split(X, Y, test_size=0.3, random_state=999, stratify=Y)\n",
    "# 定义数据集\n",
    "Y_train = data_train_y\n",
    "X_train = data_train_x.drop(columns = drop_vars)\n",
    "Y_test = data_test_y\n",
    "X_test = data_test_x.drop(columns = drop_vars)\n",
    "# 转换成lgb支持的格式\n",
    "W_train = np.ones(X_train.shape[0])\n",
    "W_test = np.ones(X_test.shape[0])\n",
    "lgb_train = lgb.Dataset(X_train,Y_train,weight = W_train,free_raw_data = False)\n",
    "lgb_eval = lgb.Dataset(X_test,Y_test,reference = lgb_train,weight = W_test,free_raw_data = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "object_vars = X_train.select_dtypes(include='O').columns.tolist()\n",
    "X_train.drop(columns=object_vars, inplace=True)\n",
    "X_test.drop(columns=object_vars, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义超参数优化（贝叶斯优化——Tree Parzen估计）\n",
    "#objective函数里设计了三种loss funtion的形式，不同的loss funtion 会导致收敛速度不一样。还需要研究怎么设计loss function能比较快得达到最优。\n",
    "from hyperopt import STATUS_OK\n",
    "from timeit import default_timer as timer\n",
    "MAX_EVALS = 100\n",
    "N_FOLDS = 5\n",
    "\n",
    "\n",
    "###定义超参数优化的目标函数\n",
    "def objective(params, n_folds = N_FOLDS):\n",
    "    \"\"\"Objective function for Gradient Boosting Machine Hyperparameter Optimization\"\"\"\n",
    "    \n",
    "    # Keep track of evals\n",
    "    global ITERATION\n",
    "    \n",
    "    ITERATION += 1\n",
    "    \n",
    "    # Retrieve the subsample if present otherwise set to 1.0\n",
    "    subsample = params['boosting_type'].get('bagging_fraction', 1.0)\n",
    "    \n",
    "    # Extract the boosting type\n",
    "    params['boosting_type'] = params['boosting_type']['boosting_type']\n",
    "    params['bagging_fraction'] = subsample\n",
    "    \n",
    "    # Make sure parameters that need to be integers are integers\n",
    "    for parameter_name in ['num_leaves', 'bagging_freq', 'min_data_in_leaf', 'max_bin', 'max_depth', 'n_estimators']:\n",
    "        params[parameter_name] = int(params[parameter_name])\n",
    "    params['num_leaves'] = min((params['num_leaves'], 2 ** params['max_depth']))\n",
    "    for parameter_name in ['learning_rate', 'bagging_fraction', 'feature_fraction', 'reg_lambda', 'reg_alpha']:\n",
    "        params[parameter_name] = round(params[parameter_name], 4)\n",
    "    start = timer()\n",
    "     \n",
    "    # Perform n_folds cross validation\n",
    "    lgb_model = lgb.LGBMClassifier(n_jobs = -1, \n",
    "                                       objective = 'binary', random_state = 50 , **params)\n",
    "    lgb_model.fit(X_train, Y_train)\n",
    "    pred1 = lgb_model.predict_proba(X_train)[:,1]\n",
    "    pred2 = lgb_model.predict_proba(X_test)[:,1]\n",
    "    auc1 = roc_auc_score(Y_train, pred1)\n",
    "    auc2 = roc_auc_score(Y_test, pred2)\n",
    "    run_time = timer() - start\n",
    "    \n",
    "    # Extract the best score\n",
    "    loss = abs(auc1 - auc2) / auc2 - auc2\n",
    "    \n",
    "    # Dictionary with information for evaluation\n",
    "    return {'loss': loss, 'params': params, 'iteration': ITERATION,\n",
    "            'train_time': run_time, 'status': STATUS_OK}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "###定义搜索域\n",
    "\n",
    "'''\n",
    "hp.choice返回一个选项，选项可以是list或者tuple.options可以是嵌套的表达式，用于组成条件参数。 \n",
    "hp.pchoice(label,p_options)以一定的概率返回一个p_options的一个选项。这个选项使得函数在搜索过程中对每个选项的可能性不均匀。 \n",
    "hp.uniform(label,low,high)参数在low和high之间均匀分布。 \n",
    "hp.quniform(label,low,high,q),参数的取值是round(uniform(low,high)/q)*q，适用于那些离散的取值。 \n",
    "hp.loguniform(label,low,high)绘制exp(uniform(low,high)),变量的取值范围是[exp(low),exp(high)] \n",
    "hp.randint(label,upper) 返回一个在[0,upper)前闭后开的区间内的随机整数\n",
    "'''\n",
    "from hyperopt import hp\n",
    "from hyperopt.pyll.stochastic import sample\n",
    "space = {\n",
    "#     'boosting_type': hp.choice('boosting_type', [{'boosting_type': 'gbdt', 'bagging_fraction': hp.uniform('gbdt_bagging_fraction', 0.5, 1)}, \n",
    "#                                                  {'boosting_type': 'dart', 'bagging_fraction': hp.uniform('dart_bagging_fraction', 0.5, 1)},\n",
    "#                                                  {'boosting_type': 'goss', 'bagging_fraction': 1.0}]),\n",
    "    'boosting_type': hp.choice('boosting_type', [{'boosting_type': 'gbdt', 'bagging_fraction': hp.uniform('gbdt_bagging_fraction', 0.5, 1)}]),\n",
    "    'num_leaves': hp.quniform('num_leaves', 4, 32, 1),\n",
    "    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),\n",
    "    'bagging_freq': hp.uniform('bagging_freq', 0, 5),\n",
    "    'feature_fraction': hp.uniform('feature_fraction', 0.5, 1),\n",
    "    'min_data_in_leaf': hp.uniform('min_data_in_leaf', 10, 100),\n",
    "    'max_bin': hp.uniform('max_bin', 4, 20),\n",
    "    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),\n",
    "    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0),\n",
    "    'importance_type': 'gain',\n",
    "    'max_depth': hp.uniform('max_depth', 2, 6),\n",
    "    'n_estimators': hp.uniform('n_estimators', 50, 200),\n",
    "#     'min_gain_to_split': hp.uniform('n_estimators', 0, 100),\n",
    "    #     'subsample_for_bin': hp.quniform('subsample_for_bin', 20000, 300000, 20000),\n",
    "#     'min_child_samples': hp.quniform('min_child_samples', 20, 500, 5),\n",
    "#     'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),\n",
    "#     'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0),\n",
    "#     'colsample_bytree': hp.uniform('colsample_by_tree', 0.6, 1.0)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.81, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.81               \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=87, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=87                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6681, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6681                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.8547, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8547           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=66, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=66                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.5625, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5625                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=0, subsample_freq=0 will be ignored. Current value: bagging_freq=0                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.8252, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8252           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=54, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=54                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9363, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9363                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=0, subsample_freq=0 will be ignored. Current value: bagging_freq=0                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5788, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5788           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=48, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=48                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6918, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6918                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7141, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7141           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=63, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=63                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6616, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6616                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.9172, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9172           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=53, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=53                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6139, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6139                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.56, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.56               \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=84, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=84                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7381, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7381                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.8695, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8695           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=62, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=62                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9391, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9391                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=0, subsample_freq=0 will be ignored. Current value: bagging_freq=0                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7136, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7136           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=95, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=95                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.679, subsample=1.0 will be ignored. Current value: bagging_fraction=0.679                    \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=0, subsample_freq=0 will be ignored. Current value: bagging_freq=0                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.81, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.81               \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=85, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=85                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7978, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7978                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5515, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5515           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=72, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=72                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.528, subsample=1.0 will be ignored. Current value: bagging_fraction=0.528                    \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7399, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7399           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=29, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=29                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7643, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7643                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=0, subsample_freq=0 will be ignored. Current value: bagging_freq=0                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6628, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6628           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=62, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=62                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7079, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7079                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6934, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6934           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=92, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=92                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6629, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6629                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.8802, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8802           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=84, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=84                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6229, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6229                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5997, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5997           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=15, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=15                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6318, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6318                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7422, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7422           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=41, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=41                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.5535, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5535                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6119, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6119           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=36, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=36                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.5448, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5448                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=0, subsample_freq=0 will be ignored. Current value: bagging_freq=0                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.9837, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9837           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=11, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=11                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9879, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9879                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=0, subsample_freq=0 will be ignored. Current value: bagging_freq=0                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5597, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5597           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9201, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9201                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5243, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5243           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=44, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=44                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8202, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8202                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5028, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5028           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=24, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=24                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.835, subsample=1.0 will be ignored. Current value: bagging_fraction=0.835                    \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5153, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5153           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=24, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=24                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8372, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8372                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5187, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5187           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=22, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=22                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8663, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8663                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6407, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6407           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=26, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=26                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8746, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8746                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5086, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5086           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=21, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=21                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8441, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8441                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5062, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5062           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=34, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=34                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9917, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9917                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6539, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6539           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=17, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=17                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9026, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9026                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7766, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7766           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=31, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=31                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7663, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7663                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.9838, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9838           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=40, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=40                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8018, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8018                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6113, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6113           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=11, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=11                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9641, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9641                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5311, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5311           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=25, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=25                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7298, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7298                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5006, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5006           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=48, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=48                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8969, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8969                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5924, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5924           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=73, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=73                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8371, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8371                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6319           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=36, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=36                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7914, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7914                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.9378, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9378           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=16, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=16                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8677, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8677                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6938, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6938           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=58, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=58                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9527, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9527                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5783, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5783           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=48, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=48                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.5855, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5855                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7695, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7695           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=29, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=29                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7121, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7121                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.8323, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8323           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=55, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=55                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7674, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7674                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5577, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5577           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=20, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=20                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.834, subsample=1.0 will be ignored. Current value: bagging_fraction=0.834                    \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5405, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5405           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=70, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=70                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9236, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9236                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6862, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6862           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=14, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=14                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8945, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8945                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5787, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5787           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=76, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=76                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7406, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7406                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6699, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6699           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=50, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=50                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7868, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7868                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7155, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7155           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=26, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=26                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6901, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6901                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.9124, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9124           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=43, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=43                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.641, subsample=1.0 will be ignored. Current value: bagging_fraction=0.641                    \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7841, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7841           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=99, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=99                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.971, subsample=1.0 will be ignored. Current value: bagging_fraction=0.971                    \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=0, subsample_freq=0 will be ignored. Current value: bagging_freq=0                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.634, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.634             \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=58, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=58                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8181, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8181                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.8551, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8551           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=38, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=38                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.5858, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5858                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7226, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7226           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=32, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=32                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7173, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7173                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.535, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.535             \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=12, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=12                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7514, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7514                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6185, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6185           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=66, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=66                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9317, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9317                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5892, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5892           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=45, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=45                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6525, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6525                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.8001, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8001           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=23, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=23                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.852, subsample=1.0 will be ignored. Current value: bagging_fraction=0.852                    \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5688, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5688           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=19, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=19                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8168, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8168                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7588, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7588           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=52, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=52                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8884, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8884                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5433, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5433           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=31, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=31                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6771, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6771                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5098, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5098           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=79, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=79                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8539, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8539                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.9506, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9506           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=37, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=37                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.5076, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5076                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6564, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6564           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=26, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=26                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9488, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9488                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.8921, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8921           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=90, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=90                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9166, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9166                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6828, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6828           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=41, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=41                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7742, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7742                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5217, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5217           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=28, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=28                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9997, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9997                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.729, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.729             \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=34, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=34                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7007, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7007                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5152, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5152           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=22, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=22                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8822, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8822                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6048, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6048           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=14, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=14                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8613, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8613                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5019, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5019           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=23, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=23                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8046, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8046                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5531, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5531           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=16, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=16                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7833, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7833                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=0, subsample_freq=0 will be ignored. Current value: bagging_freq=0                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6238, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6238           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=33, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=33                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9779, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9779                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=0, subsample_freq=0 will be ignored. Current value: bagging_freq=0                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5632, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5632           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=28, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=28                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8337, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8337                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5004, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5004           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=39, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=39                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.874, subsample=1.0 will be ignored. Current value: bagging_fraction=0.874                    \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5254, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5254           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.754, subsample=1.0 will be ignored. Current value: bagging_fraction=0.754                    \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6485, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6485           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=19, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=19                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9037, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9037                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5941, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5941           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=46, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=46                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7294, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7294                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5786, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5786           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=42, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=42                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9116, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9116                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=0, subsample_freq=0 will be ignored. Current value: bagging_freq=0                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.544, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.544             \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=21, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=21                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8038, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8038                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5692, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5692           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=30, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=30                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9437, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9437                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6714, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6714           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=25, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=25                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8248, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8248                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6732, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6732           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=35, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=35                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8365, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8365                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7028, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7028           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=25, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=25                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8247, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8247                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.8115, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8115           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=50, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=50                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8481, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8481                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7478, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7478           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=57, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=57                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7806, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7806                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.8358, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8358           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=18, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=18                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9611, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9611                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.645, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.645             \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=62, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=62                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.737                    \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.703, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.703             \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=15, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=15                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7624, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7624                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.626, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.626             \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=38, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=38                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7956, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7956                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7947, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7947           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=46, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=46                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8753, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8753                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.9704, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9704           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=12, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=12                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8171, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8171                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6111, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6111           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=33, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=33                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7184, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7184                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7335, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7335           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=60, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=60                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6939, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6939                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5837, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5837           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=54, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=54                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6168, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6168                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.7584, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.7584           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=66, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=66                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9267, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9267                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.8805, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8805           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=27, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=27                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8108, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8108                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6614, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6614           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=30, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=30                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.6754, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6754                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5361, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5361           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=24, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=24                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.855, subsample=1.0 will be ignored. Current value: bagging_fraction=0.855                    \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5981, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5981           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=17, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=17                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.8838, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8838                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6376, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6376           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=81, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=81                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9053, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9053                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.6762, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6762           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=70, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=70                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.9339, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9339                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "feature_fraction is set=0.5123, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5123           \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "min_data_in_leaf is set=36, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=36                   \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_fraction is set=0.7605, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7605                  \n",
      "[LightGBM] [Warning]                                                                                                   \n",
      "bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3                                 \n",
      "100%|█████████████████████████████████████████████| 100/100 [01:59<00:00,  1.19s/trial, best loss: -0.7692008345994636]\n"
     ]
    }
   ],
   "source": [
    "#定义优化函数\n",
    "from hyperopt import tpe\n",
    "#定义结果存储变量\n",
    "from hyperopt import Trials\n",
    "# Keep track of results\n",
    "bayes_trials = Trials()\n",
    "#定义中间结果存储文件\n",
    "# out_file = 'results/gbm_trials.csv'\n",
    "# of_connection = open(out_file, 'w')\n",
    "# writer = csv.writer(of_connection)\n",
    "\n",
    "# # Write the headers to the file\n",
    "# writer.writerow(['loss', 'params', 'iteration', 'estimators', 'train_time'])\n",
    "# of_connection.close()\n",
    "\n",
    "### 求最优解\n",
    "from hyperopt import fmin\n",
    "# Global variable\n",
    "global  ITERATION\n",
    "\n",
    "ITERATION = 0\n",
    "\n",
    "# Run optimization\n",
    "best = fmin(fn = objective, space = space, algo = tpe.suggest, \n",
    "            max_evals = MAX_EVALS, trials = bayes_trials, rstate = np.random.RandomState(123))\n",
    "#按loss从小到大排序\n",
    "# Sort the trials with lowest loss (highest AUC) first\n",
    "bayes_trials_results = sorted(bayes_trials.results, key = lambda x: x['loss'])\n",
    "bayes_trials_results[:2]\n",
    "#结果\n",
    "# results = pd.read_csv('d:/gbm_trials.csv')\n",
    "# Sort with best scores on top and reset index for slicing\n",
    "# results.sort_values('loss', ascending = True, inplace = True)\n",
    "# results.reset_index(inplace = True, drop = True)\n",
    "# results.head()\n",
    "# import ast\n",
    "# Convert from a string to a dictionary\n",
    "# ast.literal_eval(bayes_trials_results.loc[0, 'params'])\n",
    "#获取最优的超参数\n",
    "# best_bayes_estimators = int(bayes_trials_results[0]['estimators'])\n",
    "best_bayes_params = bayes_trials_results[0]['params']\n",
    "# Re-create the best model and train on the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bagging_fraction': 0.8372,\n",
       " 'bagging_freq': 3,\n",
       " 'boosting_type': 'gbdt',\n",
       " 'feature_fraction': 0.5153,\n",
       " 'importance_type': 'gain',\n",
       " 'learning_rate': 0.0225,\n",
       " 'max_bin': 15,\n",
       " 'max_depth': 2,\n",
       " 'min_data_in_leaf': 24,\n",
       " 'n_estimators': 90,\n",
       " 'num_leaves': 4,\n",
       " 'reg_alpha': 0.4626,\n",
       " 'reg_lambda': 0.5525}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_bayes_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################## 手动调整参数 #####################################\n",
    "best_bayes_params = {'bagging_fraction': 0.8372,\n",
    " 'bagging_freq': 3,\n",
    " 'boosting_type': 'gbdt',\n",
    " 'feature_fraction': 0.5153,\n",
    " 'importance_type': 'gain',\n",
    " 'learning_rate': 0.0225,\n",
    " 'max_bin': 15,\n",
    " 'max_depth': 2,\n",
    " 'min_data_in_leaf': 24,\n",
    " 'n_estimators': 90,\n",
    " 'num_leaves': 4,\n",
    " 'reg_alpha': 0.4626,\n",
    " 'reg_lambda': 0.5525}\n",
    "# 'min_gain_to_split': 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training...\n",
      "[LightGBM] [Warning] feature_fraction is set=0.5153, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5153\n",
      "[LightGBM] [Warning] min_data_in_leaf is set=24, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=24\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8372, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8372\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "train over\n",
      "训练时间为： 1.0502004623413086\n"
     ]
    }
   ],
   "source": [
    "################################## 用最优超参数训练模型 #####################################\n",
    "print('Starting training...')\n",
    "start_time = time.time()\n",
    "best_bayes_model = lgb.LGBMClassifier(n_jobs = -1, \n",
    "                                       objective = 'binary', random_state = 50, **best_bayes_params)\n",
    "best_bayes_model.fit(X_train, Y_train, eval_metric='auc', verbose=2)\n",
    "# gbm.fit(X_train, Y_train)\n",
    "# gbm = lgb.train(params, lgb_train, num_boost_round=10, feature_name = 'auto')\n",
    "print('train over')\n",
    "end_time = time.time()\n",
    "print('训练时间为：', end_time - start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS(Train): 0.405034\n",
      "KS(Test): 0.402629\n",
      "AUC Score(Train): 0.774199\n",
      "AUC Score(Test): 0.772021\n"
     ]
    }
   ],
   "source": [
    "################################## 模型效果 #####################################\n",
    "# get the predicted class\n",
    "# bst = lgb.Booster(model_file='model.txt')\n",
    "\n",
    "train_class_pred = best_bayes_model.predict_proba(X_train)[:, 1]\n",
    "test_class_pred = best_bayes_model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "from sklearn.metrics import roc_curve\n",
    "fpr_train, tpr_train, thresholds_test = roc_curve(np.array(Y_train), train_class_pred)\n",
    "fpr_test, tpr_test, thresholds_test = roc_curve(np.array(Y_test), test_class_pred)\n",
    "# df = pd.DataFrame({'FPR': fpr_test, 'TPR': tpr_test, 'Thresholds': thresholds_test})\n",
    "# print(df)\n",
    "ks_train = max(tpr_train - fpr_train)\n",
    "ks_test = max(tpr_test - fpr_test)\n",
    "print(\"KS(Train): %f\" % ks_train)\n",
    "print(\"KS(Test): %f\" % ks_test)\n",
    "\n",
    "# get the auc\n",
    "from sklearn.metrics import roc_auc_score\n",
    "print(\"AUC Score(Train): %f\" % roc_auc_score(Y_train, train_class_pred))\n",
    "print(\"AUC Score(Test): %f\" % roc_auc_score(Y_test, test_class_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       feature_name     element\n",
      "92                 ir_allmatch_days  595.628193\n",
      "365          als_m3_id_bank_selfnum  576.358102\n",
      "103                    pd_id_gender  462.550741\n",
      "384        als_m3_cell_bank_selfnum  407.793153\n",
      "897  alf_apirisk_time_lenth_all_sum  261.160498\n",
      "..                              ...         ...\n",
      "487    als_m12_id_bank_night_orgnum    3.569460\n",
      "654            tl_cell_t1_nbank_num    3.472390\n",
      "211                      mma_var149    3.295630\n",
      "970                cf_cons_C20_nums    2.305070\n",
      "271                      mma_var238    0.201951\n",
      "\n",
      "[83 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "################################## Feature Importance #####################################\n",
    "feature_imp = pd.Series(best_bayes_model.feature_importances_)\n",
    "feature_name = best_bayes_model._Booster.feature_name()\n",
    "feature_df = pd.DataFrame({'feature_name': feature_name, 'element': feature_imp})\n",
    "feature_df = feature_df.sort_values(by = 'element',ascending = False)\n",
    "feature_df = feature_df[feature_df['element'] > 0]\n",
    "print(feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(feature_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 变量筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PSI偏高的被剔除的变量：\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "X_train1 = X_train[feature_df.feature_name.tolist()]\n",
    "X_test1 = X_test[feature_df.feature_name.tolist()]\n",
    "#oot_X = oot_X[feature_df.feature_name.tolist()]\n",
    "cat_var = X_train1.select_dtypes('category').columns.tolist()\n",
    "#drop_vars1 = psi_var(X_train1, oot_X)\n",
    "drop_vars2 = psi_var(X_train1.drop(columns = cat_var), X_test1.drop(columns = cat_var))\n",
    "drop_vars = list(set(drop_vars2))#| set(drop_vars1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training...\n",
      "training...\n",
      "[LightGBM] [Warning] feature_fraction is set=0.5153, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5153\n",
      "[LightGBM] [Warning] min_data_in_leaf is set=24, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=24\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8372, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8372\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "train over\n",
      "Starting training...\n",
      "training...\n",
      "train over\n",
      "Starting training...\n",
      "training...\n",
      "train over\n",
      "Starting training...\n",
      "training...\n",
      "train over\n",
      "Starting training...\n",
      "training...\n",
      "train over\n",
      "Starting training...\n",
      "training...\n",
      "train over\n"
     ]
    }
   ],
   "source": [
    "# 循环筛选：每次遍历都剔除feature_importance==0的变量\n",
    "drop_vars = []\n",
    "X_train_final = X_train1.drop(columns = drop_vars)\n",
    "while True:\n",
    "    X_test_final = X_test[X_train_final.columns]\n",
    "    num_train, num_feature = X_train_final.shape\n",
    "    W_train = np.ones(X_train_final.shape[0])\n",
    "    W_test = np.ones(X_test_final.shape[0])\n",
    "\n",
    "    # create dataset for lightgbm\n",
    "    # if you want to re-use data, remember to set free_raw_data = False\n",
    "    lgb_train = lgb.Dataset(X_train_final, Y_train, weight=W_train, free_raw_data=False)\n",
    "    lgb_eval = lgb.Dataset(X_test_final, Y_test, reference=lgb_train, weight=W_test, free_raw_data=False)\n",
    "\n",
    "    # import xgboost as xgb\n",
    "\n",
    "    print('Starting training...')\n",
    "    start_time = time.time()\n",
    "    print('training...')\n",
    "\n",
    "    # gbm.fit(X_train, Y_train)\n",
    "    gbm = lgb.LGBMClassifier(n_jobs = -1, \n",
    "                                           objective = 'binary', random_state = 50, **best_bayes_params)\n",
    "    gbm.fit(X_train_final, Y_train)\n",
    "    print('train over')\n",
    "    end_time = time.time()\n",
    "    feature_imp = pd.Series(gbm.feature_importances_)\n",
    "    feature_name = pd.Series(gbm.booster_.feature_name())\n",
    "    feature_df = pd.DataFrame({'feature_name': feature_name, 'element': feature_imp})\n",
    "#     feature_df = feature_df.sort_values(by='element', ascending=False)\n",
    "    feature_df = feature_df[feature_df['element'] > 0]\n",
    "    if len(feature_imp) == len(feature_df):\n",
    "        break\n",
    "    else:\n",
    "        X_train_final = X_train[feature_df.feature_name.tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       feature_name     element\n",
      "0                  ir_allmatch_days  665.692395\n",
      "1            als_m3_id_bank_selfnum  727.514696\n",
      "2                      pd_id_gender  511.746922\n",
      "3          als_m3_cell_bank_selfnum  333.633402\n",
      "4    alf_apirisk_time_lenth_all_sum  295.765798\n",
      "5           als_lst_id_bank_inteday  198.921599\n",
      "6             alf_ict_lenth_all_sum  250.007202\n",
      "7    alf_apirisk_time_slope_all_sum  189.722600\n",
      "8         als_lst_cell_bank_inteday  158.157542\n",
      "9              pd_id_city_rent_sort  135.862599\n",
      "10  alf_apirisk_time_slope_all_mean  177.608899\n",
      "11        als_m12_cell_bank_selfnum  314.178796\n",
      "12                  pd_id_apply_age  184.185198\n",
      "13               pd_cell_city_level  136.952700\n",
      "14         als_m6_cell_bank_selfnum   97.540499\n",
      "15            als_m6_id_min_inteday   33.712799\n",
      "16                   alf_count_cell   62.450800\n",
      "17                      cf_prob_max   71.756600\n",
      "18           alf_ict_lenth_d360_sum  131.070501\n",
      "19           als_m12_id_caon_allnum   88.999801\n",
      "20            ql_m12_id_bank_allnum   66.718380\n",
      "21           als_m1_id_bank_selfnum   22.076270\n",
      "22           alf_ict_lenth_d180_sum  103.674201\n",
      "23        pd_id_city_new_house_sort   75.218190\n",
      "24          ql_m12_id_bank_allnum_d   20.243799\n",
      "25                 ql_m12_id_orgnum   66.024199\n",
      "26                       mma_var345   33.912471\n",
      "27   als_m12_cell_nbank_max_inteday   28.258801\n",
      "28             tl_id_t2_nbank_reamt   12.924460\n",
      "29                       mma_var342   45.451820\n",
      "30                      cf_prob_min   20.681080\n",
      "31             tl_id_t4_nbank_reamt   38.054299\n",
      "32     als_m12_cell_bank_tra_orgnum   13.612300\n",
      "33   ql_m12_id_bank_national_allnum   36.244860\n",
      "34                       mma_var123   24.568030\n",
      "35                     cf_prob_cell   26.781120\n",
      "36          ql_m12_cell_bank_allnum   24.263201\n",
      "37             ql_m12_id_max_monnum   53.202590\n",
      "38               cf_cons_C29_amount   31.603290\n",
      "39                       mma_var149   10.494780\n"
     ]
    }
   ],
   "source": [
    "feature_imp = pd.Series(gbm.feature_importances_)\n",
    "feature_name = pd.Series(gbm.booster_.feature_name())\n",
    "feature_df = pd.DataFrame({'feature_name': feature_name, 'element': feature_imp})\n",
    "# feature_df = feature_df.reset_index().drop(columns=['index'])\n",
    "# feature_df = feature_df.sort_values(by='element', ascending=False)\n",
    "feature_df = feature_df[feature_df['element'] > 0]\n",
    "print(feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    " X_train_final = X_train[feature_df.feature_name.tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "mannual_drops=[]\n",
    "# mannual_drops=['flag_profilepopulation','flag_fraudrelation_g','flag_graylist', 'pp_birthyear', 'pp_age', 'pp_gender', 'cf_cons_C13_views']\n",
    "X_train_final1 = X_train_final.drop(columns=mannual_drops)\n",
    "X_test_final = X_test[X_train_final1.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################## 手动调整参数 #####################################\n",
    "best_bayes_params = {'bagging_fraction': 0.8372,\n",
    " 'bagging_freq': 3,\n",
    " 'boosting_type': 'gbdt',\n",
    " 'feature_fraction': 0.5153,\n",
    " 'importance_type': 'gain',\n",
    " 'learning_rate': 0.0225,\n",
    " 'max_bin': 15,\n",
    " 'max_depth': 3,\n",
    " 'min_data_in_leaf': 24,\n",
    " 'n_estimators': 90,\n",
    " 'num_leaves': 4,\n",
    " 'reg_alpha': 0.4626,\n",
    " 'reg_lambda': 0.5525}\n",
    "# 'min_gain_to_split': 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(bagging_fraction=0.8372, bagging_freq=3, feature_fraction=0.5153,\n",
       "               importance_type='gain', learning_rate=0.0225, max_bin=15,\n",
       "               max_depth=3, min_data_in_leaf=24, n_estimators=90, num_leaves=4,\n",
       "               objective='binary', random_state=50, reg_alpha=0.4626,\n",
       "               reg_lambda=0.5525)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "# gbm.fit(X_train, Y_train)\n",
    "gbm = lgb.LGBMClassifier(n_jobs = -1, \n",
    "                                       objective = 'binary', random_state = 50, **best_bayes_params)\n",
    "gbm.fit(X_train_final, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS(Train): 0.398742\n",
      "KS(Test): 0.409880\n",
      "AUC Score(Train): 0.771959\n",
      "AUC Score(Test): 0.770732\n"
     ]
    }
   ],
   "source": [
    "bst = gbm\n",
    "\n",
    "train_class_pred = bst.predict_proba(X_train_final)[:,1]\n",
    "test_class_pred = bst.predict_proba(X_test_final)[:,1]\n",
    "\n",
    "from sklearn.metrics import roc_curve\n",
    "fpr_train, tpr_train, thresholds_test = roc_curve(np.array(Y_train), train_class_pred)\n",
    "fpr_test, tpr_test, thresholds_test = roc_curve(np.array(Y_test), test_class_pred)\n",
    "\n",
    "ks_train = max(tpr_train - fpr_train)\n",
    "ks_test = max(tpr_test - fpr_test)\n",
    "print(\"KS(Train): %f\" % ks_train)\n",
    "print(\"KS(Test): %f\" % ks_test)\n",
    "\n",
    "# get the auc\n",
    "from sklearn.metrics import roc_auc_score\n",
    "print(\"AUC Score(Train): %f\" % roc_auc_score(Y_train, train_class_pred))\n",
    "print(\"AUC Score(Test): %f\" % roc_auc_score(Y_test, test_class_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4x8aQJk0aqlSpQuXKlalWrRq7du1K1v3jxo3jo48+cpF0zmPx4sWULasH4bfffuuVSuLUqVO0aNGCbNmysXbtWrJly2a1SAY3ERYWRrp06fjll18oWrQkFSp4v5LwO38OlCKyWAmurNrtcUoCjKKIIUOGDBw4cIA//viDiRMn8uabb1otklOJjIykR48edO7cGYC5c+fSs2dPi6VKPpcvX6ZZs2bcu3eP9evXU6hQIatFMriJ//u//4uJCJAjR26WLv3do3NFOIJ/0GFyt3icRyaN0QUeamMziiIebty4Qfbs2QG4desWjRo1olq1alSsWJHly5fH1HvvvfcoXbo0Tz75JMeOHbNK3CSJiooie/bsfP/99wBs3bqV559/3mKpkk9ISAitWrXi7NmzrFq1yvLc2wb38fbbbzNw4EAuXrzIiBGT2L37PHnzPmK1WCnC3nAd2rFH0jdYiJfrY+cRGhpKlSpVCAsL4/z582zZsgWA9OnTs3TpUrJkycKVK1eoXbs2bdu25ffffycwMJADBw4QERFBtWrVYsJdeBpffPEFN2/epFy5cvz+++9euZ5/9+5d2rdvz/79+1myZAlPPPGE1SIZ3ERUVBRfffUVAQHpmD9/N7VqVfbazXPRxPVu8sTlJnsS/HOLSI7EDkcaF5HmInJMRE6IyBvxXC8iIltFZL+IHBQRy/JTRi89HT16lHXr1tGrVy+UUiilGD16NJUqVaJx48acO3eOixcvsnPnTtq3b0/GjBnJkiWLR3oNKaUICwvj5ZdfBmDfvn1eqSSioqLo3bs3W7du5euvv35gc6AVeNPY9mbCw8MZOnQo58+fZ8KE2dSp4/1Kgrt3ydGjhdcoCUh8RrEPUIAARYBg2/tswL/Ao4k1LCJpgGlAE+AssEdEViilDttVexv4QSk1Q0TKA2uAYg/1SZxInTp1uHLlCpcvX2bNmjVcvnyZffv2kTZtWooVK0ZYWJjVIjpEs2bN2LhxIwD9+vUjQ4YMFkv0cIwcOZLAwEA+/PDDWOFFrMKbx7Y3Yb9PAqBRo2cslMaJpEtHyIf/R2ThYl6hJCCRGYVS6lGlVHFgE9BGKZVLKZUTaA1scKDtmsAJpdTfSql7QCAQ9z+tgOhth1mB/5L7AVzB0aNHiYyMJGfOnISEhJAnTx7Spk3L1q1bOX36NAD169dn2bJlhIaGcvPmTVauXGmx1PeJjIyke/fubNy4kaZNm/Lxxx8zc+ZMq8V6KD7++GOmTJnCsGHDeP31160WJxqvHdvewtKlSxkyZAhZsmTjpZfeZeHCPeTP7/02ifTLFwJwt2Fzr1ES4JiNorZSqn/0iVJqrYhMcuC+gsAZu/OzQK04dcYBG0RkGJAJaBxfQyIyABgAUKRIEQe6Tj7RNgrQSzbffPMNadKk4bnnnqNNmzZUrFiR6tWrx7iWVqtWja5du1K5cmXy5MlDjRo1XCLXwzBixAgCAwOB2J4i3sbChQt59dVX6dixIx9//LEn7br2qrHtbRw/fpyuXbsC0L//Wwwf/pqnOgM5TIxNIo0/d5u2QWXIaLVIycIRRfGfiLwNfGc7fw7nPR11B+YqpaaISB1gnohUUEpF2VdSSs0CZgFUr17dJamdIiMj4y3PlSsXv/zyS7zX3nrrLd566y1XiPNQnDx5kvfee4+vv/4a0LkZMmb0rgEZzbZt22J2XX/33XfeuOvaY8a2t3DlyhW++eYbXnvtNQAWLz5ArVqVfUdJiHBl4WavUxLgmKLoDowFltrOd9jKkuIcUNjuvJCtzJ4XgOYASqlfRCQ9kAu45ED7BjsOHjxI5cqVY863b9/utUri0KFDtGvXjpIlS7J8+XJPNMCbse1k7t27R+7cuWPOn3nmeWrW9H7DdSwlsWibJUmHnEGS/wal1DWl1MtKqaq242Wl1DUH2t4DlBKRR0UkAOgGrIhT51+gEYCIlAPSA5eT9xEMQUFBMUpi0qRJ3L17l/r161ss1cNx5swZWrRoQaZMmVi7dm3MfhYPw4xtJxIVFUW6dOkAKFWqArt2XeSLL+bifZPIB0m3ebXXKwlwYEYhIrmBkcBj6MEOgFLq6cTuU0pFiMhQYD2QBvhKKfWXiIwH9iqlVgAjgC9FZDja+NdbKTdnDfdyli9fTt++fQEYP368Jxl8k01wcDAtWrTg5s2b7Ny502PX7M3Ydh63b9+mW7duMedLl+4le/Z0FkrkJKKiwM+P2wNfI7RrH6Jy5LJaohThyNLT98BCtLfTQOB5HHwyUkqtQbsF2pe9Y/f+MFDXUWENGqUU69evp0+fPly4cAGAp59+mjFjxlgs2cMTFhZGu3btCAoKYv369VSqVMlqkRLFjO2Uce7cOZYuXcr777/P+fPnAdi+/aJPKAn/oMNkf7EzwV/MJ+Kxyl6vJMAxRZFTKTVHRF5WSm0HtovIHlcLZoifyMhIatWqxb59+wAoW7Ysq1at8lrPJtBLDz179mTHjh0sWLCAhg0bWi2SwUUopRg1ahSffvop9+7do2rV2nz44QIqV36cnDkzWy1eivEPOkzOzg3Bzw+VzuNsaw+NI4oi3PZ6XkRaoT2evC/kqI8wcuRI9u3bR0BAAPv27aNChQpWi5QilFIMHz6cxYsXM2XKlFjLEAbf47333mPy5MlUrVqHUaOmUqNGLa/PIxGNvZLwdptEXBxRFO+KSFb0muvn6E1Ew10qlSFejh07xtSpUwG9nu+tXk32TJkyhc8++4zhw4fz6quvWi2OwYVcvXo1Znn0k0+WULJkPoslch5p/jnps0oCHPN6WqWUClFK/amUaqiUetxmrDO4ifDwcHbu3Bmz2W/z5s0+oSTmz5/P66+/TpcuXbwil4fh4bh37x6//voruXLptfrBg8dRooTvKAmAyHwFuduguU8qCUg8KGB6EXleRNqKZpSIrBKRT0XE+60zXoJSip49e8a4u/bo0YOnn07U4cwr2Lx5M71796ZBgwZ8++23+Hm7w7whXpRStG/fnjp16gCQO3c+hg170+s30UWT5sQx5HowpE/P9U+/8UklAYkvPX2Ltk9kQi87/Ql8ATwJzEV7QRlcxLFjx1i1ahVjx47l9u3b+Pn5sW3bNurW9X5Hml9//ZW2bdtSpkwZli5dGuNDb/ANoqKi+OWXX9i+fXusyAVffrmJ2rXrkCVLgIXSOY9om0R4lZpc+8ZzYr25gsQURXmlVAUR8QfOKqWespWvE5E/3CBbquTWrVtUqFAhJvggwFNPPcVXX31F8eLFLZTMORw5coRWrVqRP39+Nm7caNKY+hj//PMPjz4aO7B07tz5mTFjDbVrV/GZmYS94frGmMlWi+NyElMU9yBmc1Hc2E7xB0YypIioqCjq1q3L6dOnKVOmDFOmTKFOnTpemdc6Ps6cOUOzZs0ICAhgw4YN5MvnW+vUqZ3Q0NAYJVG1al2GDh1P5cpVyJMnh0/sso7GXkl4Sz6JlJKYoigkIp+hc1BEv8d2XtDlkqUyNm7cSM+ePbl48SL58uXj6NGjVovkVK5du0bz5s0JCQlh+/btPjE7Mtxn8+bNNG6sA+TWq9eC+fPXeH2cpnhRimyv9k1VSgISVxT2sSD2xrkW99yQAvbs2UPTpk0BKFmyJHv3+taf99atW7Ru3ZqTJ0+ybt26mHDuBt/gxx9/pEuXLgA8+WRzZs5c6ptKAkCE4BmByN2wVKMkIHFFURBYq5Ta7y5hUiMffPABb775JgDr1q2jWbNmFkvkXG7fvk2zZs3YvXs3ixYtokGDBlaLZHASUVFRVK1alYMHDwKwbdtZihcv6FPLTNH4Bx0m4w9zuTH6AyILF7NaHLeTmN4/Cbxsy/k7V0S6iohHhvL0Ru7du8fixYtjlMSSJUt8Tkncu3ePjh078uuvv7JgwQI6dOhgtUgGJ3H16lWef/55Dh48SPr0GViy5A9KlvRdJZGzy9Nk+HEefhdSZ6LCBGcUSqmF6GCAiEhVdGz9JbZ8wZuAdUqp39wipY8RFRVFvnz5CA4OBmDVqlW0atXKYqmcS0REBD169GD9+vXMmTMnZmnC4P0sXLgwVqiVFSsO8thjJS2UyHXY55O4umgrUQUKWS2SJTi0kqiU2q+UmqiUaojeP/EX0M+lkvkw7733HsHBwZQpU4affvrJ55REVFQUffv2ZdGiRXz00UcxYdANvkF0KPvnn3+N9euDUo2SSE02ibgkOKMQkcTWCZRSaoAL5PF5fvvtN955R0ej3r9/PxkyZLBYIueilGLEiBHMmzePCRMmMGLECKtFMjiR/v37c+bMGcqXr8b//jeZtGmtlsh1+F08j8qQkWvz1qRqJQGJG7Pb2F7zAE8AW2znDYFdwBIXyuWTXLt2jVq1agGwePFin1MSABMnTuSTTz7h5Zdf9qh84oaU8+OPPzJ79mwAPv30R59VEnLrJirzI9yr14hL249CgG/sJE8JCS49KaX6KKX6AGnRu7Q7KqU6ojPd+egQcR3nzp0jZ86cALzxxht07NjRYomcz2effcZbb71Fjx49mDp1KuIr23ANbN26lU6dOgHw/fd7KF++mLUCuQj/oMPkqVeaDD9+pwuMkgAcs1EUVkqdtzu/CHhmjkoPRCnFrl27KFRIG8FatmzJ+++/b7FUzue7777j5Zdfpl27dnz11VcmyJ+PcOPGDWbPnh0TiHLw4PHUq1fdYqlcQ8yOayC8sm9+xofFkW/zZhFZLyK9RaQ3sBrt9WRIgpkzZ5IhQ4aYQH69evVi5cqVPvekvWbNGvr06UPDhg1ZsGABaX11TSIVcf36dQYNGkTWrFnp378/AGPHzmbUqDG+6wKbysJyJIckExcppYaKSHugvq1ollJqqWvF8m5OnjzJqFGj+PHHHwHo2LEjI0aMiAm17Ev88ssvdOrUiYoVK7Js2TLSp/ed9I+pkej9Pc899xwA2bPnonnzbvTsOZQKFcr4pJLwu3bFKIkkcCTDHWjjdQSgALN3Iglq1KgRs0fi9OnTFCnimyt1+/bto1WrVhQsWJC1a9eSJUsWq0UypJApU6YwevRoAKpUqcMPP/xEpky+vYwYlSMXt4aN5m6DZkZJJECSI0BEuqCVQyegC7BbRDo50riINBeRYyJyQkTeSKh9ETksIn+JyPzkCO+JzJ8/n+DgYLp27cqtW7d8VkkcOnSIpk2bkiVLFjZs2EDevHmtFslt+PK4jlYS69f/w5IlP/u0kvAPOkzag/sAuN3vZaMkEsGRGcVbQA2l1CUAEcmNtlEsTuwm2w7uaUAT4CywR0RWKKUO29UpBbwJ1FVKBYtInof7GJ5BaGhozJR90qRJZMqUyWKJXMO+ffto3LgxGTNmZMuWLQ/kH/BlfHlcRwejfOSRrJQpU9Rn3V/h/ma6qOw5ubzpID65puZEHHlc8ItWEjauOnhfTeCEUupvpdQ9IBB4Jk6d/sA0pVQwQJx+vI7o4Ghjxozx2ZnEwYMHadq0KVmzZmXHjh2pMVy4z47rNWvWADB//q+pQkkgwrUvlxgl4QCO/OCvi8fraY0D9xUEztidn+XBPBalgdIi8rOI/CoizeNrSEQGiMheEdl7+fJlB7q2hujQBt27d7dYEtdw8OBBnnrqKdKnT8+WLVsoUaKE1SJZgdPGNXjO2A4PD2fs2LEAlCvnm3mfwc67SYQri7b5bI5rZ5OkolBKvQ78H1DJdsxSSo1yUv/+QCmgAdAd+FJEssUjwyylVHWlVPXcuXM7qWvnMW/ePIoWLcrOnTvJli0bZcv63lrnyZMnadasGZkzZ+ann35K9kzi4sWLPPvssxQvXpzHH3+cOnXqsHRpyp3nGjRo4In5Oxwa1+AZY7tevXoE2DaWFSlSkoAA33LftifzzI/Az88oiWTiqKXqZ2ArOozHzw7ecw4obHdeyFZmz1lghVIqXCl1CghCf8G8hjNnztCrVy/+/fdfevTowYEDB3xun8T58+dp2rQp4eHhbNiwIdk2CaUU7dq1o379+vz999/s27ePwMBAzp496yKJXYpPjesFCxbw008/AdCiRVdWrjzk0ysx1yfO4MqKX4ySSCau9HraA5QSkUdFJADoBqyIU2cZ+qkLEcmFnrL/7ajwVjNt2rQYW8Tbb78dM7PwJS5cuECjRo24ePEia9asoVy5csluY8uWLQQEBDBw4MCYsqJFizJs2DDCwsLo06cPFStWpGrVqmzduhUgwfLQ0FC6detGuXLlaN++PaGhoc75oI7jE+M6KiqKXLly8eyzzwIwZcoiZs8OJFcu39sH4x90mBzPNsPv2hVIly5VJh5KKS7zelJKRYjIUGA9kAb4Sin1l4iMB/YqpVbYrjUVkcNAJPC6Uurqw38c93Hs2DGGDh0K6KWnaG8nX+LixYs0atSI06dPs2bNGmrWrPlQ7fz1119Uq1Yt3mvTpk1DRDh06BBHjx6ladOmBAUFJVg+Y8YMMmbMyJEjRzh48GCC7boKXxnXr7/+OlevapECA/fwxBO+GbLCfse1BF+DHLmsFskrcURRPKzXE0qpNcQxfCul3rF7r4BXbYdXMWTIEEBP3e2TuPgK165do3Hjxvzzzz+sWrWKp556ymltDxkyhJ9++omAgAAKFSrEsGHDAChbtixFixYlKCiIn376Kd7yHTt28NJLLwFQqVIlKlWq5DS5HMXbx3VUVBRTp04FYOPGfylfvnASd3gn9kriyqJtRJYobbVIXosrvZ58lpMnT7J582aeeuopn1QSt27domXLlgQFBbFixQoaNmyYovYee+wxfv/995jzadOmsXnzZjzZg82XWb16NQDPPjuMcuV8VEkcPxJbSRibRIqw2uvJ64iMjKRkSZ3RyxeT8oSGhtKmTRv27t3LwoULadSoUYrbfPrppwkLC2PGjBkxZXfu3AG0x833338PQFBQEP/++y9lypRJsLx+/frMn683Ov/5558xe1cMjnHjxg3atm0LQJs2z+FjfhcxRGV6hIiSZY2ScBIOxXpSSi3BJCoC4M033wSgadOmtGnTJona3kVERARdu3Zl+/btzJs3j3bt2jmlXRFh2bJlDB8+nEmTJpE7d24yZcrEhx9+yDPPPMOgQYOoWLEi/v7+zJ07l3Tp0jF48OB4ywcNGkSfPn0oV64c5cqV4/HHH3eKjKCXZIAcTmvQA4ne+1K1al2efLKWxdI4nzTn/iUyX0GiChTi6uJt+KwmdDOil1PjuSDyApBDKTXZdn4WyAII2jg3021S2lG9enVlpd988eLFOXXqFBEREaTxIT9CpRR9+/Zl7ty5TJ8+nUGDBlktksu4ceMG06ZN49y5c7Rt25YmTZrwxRdfMGXKFE6fPn1dKZXdCrncMbajXbePHYsic2bf+hGN3nEd+kx3bvzvY6vF8SgiI6FIkfSHlAp7KKNeYktPA4Gv7M4vK6WyALnRm4hSHd988w2nTp1i8ODBPqUkAEaNGsXcuXP53//+59NKAqBnz54cO3aMihUrMnv2bBo2bMjixYtZtmwZwEmLxXMZ8+bNA6BCheo+qyQQ4XbPgUnfYEgWiS09SRyXvkUASqkwEfG9ZM9JsH79enr37g3cj7DpK0yYMIHJkyczZMgQxowZY7U4Lufvv//m0KFDAPTr14/8+fPz77//+nQujR07dtCrVy8ARo+ebrE0zsVeSRibhGtIbEaRzf5EKfU+gIj4AanKGTkyMpLmzXW4nqVLl1KwYNzQPt6JUop33nmHd955h169evHpp5/63K7y+LDPwJcmTRoKFSrk00oCiHFvHjDgbZ58sobF0jiR8HBy9G5jlISLSWxGsUFE3lVKvR2nfDywwYUyeRzRwf5efPFFpxl4PYF3332XCRMm8MILLzBr1qxUk+f6jz/+IEuWLETb50JDQ+3Pq1oqnAtYtWpVzPvRoyf4VoiOtGm5PuUrInPnM0rChSSmKF4HZovICeAPW1llYC/Qz9WCeQpXr17l44+1YWz6dN+Zsk+aNIl33nmH559/PlUpCdAzxIQQkf1uFMUtvPjii4Dege0r4cP9gw6Tdv9uQrv24V4d520GNcRPgopCKXUb6C4ixYHHbMWHlVI+a+yLy5UrVyhfvjwAc+fO9Zkf0/fff5+33nqLbt26MXv2bJ/5XI4SFhbGzJkzOXHiBJUqVaJv3774+zuaFdh7iIiIoFOnTvz3339UqVLHZ8J0xNgk0qQhrGVH1CMmBa+rcWTD3d9KqZW2I9UoCYA5c+Zw+fJlatWqFWMI9HailUSPHj347rvvfPIHMimef/559u7dS8WKFVmzZo1PbpwE2L9/P8uXLwdg4sRvPWrJad06KFgQTpzQ57t2Qdyv2CuvQPSqWXg4vP8+PFnzLs2bRFA3eBULh+9+QEkEB0O3blC3rn69fv3Bvn/+GZo0uX8UL67lAWjf/n55tWrQt68uX7IEGjeGRo2gbVv46y+n/Sm8gtT3K5EMJk6cSKFChfj111+tFsUpTJkyhbfeeovnnnuOuXPn+pyLr6McPnw4xuvphRdeeOhgh57ON998A8AXX6ykYsWSFksTm2XLoGZN/fraa0nXnzwZLh+/zp93K5EuRzhH/m8nP10o9EC9adPgySdh6FD44gt9/tZbsevUrQsbN+r3wcG6fnQoM/sUKf37Q9Om+n3hwrB4MWTLBlu2wKhR95VYaiB1rTkkg06dOhESEkLlypWtFsUpfPLJJ7z22mt07tw5VSsJiO315Kszqj///JNp06YBUL36Ex61Qfn2bdizBz76CGwTnkQJDYXvv4fJtRaRLk0EVxZtI0fNktgikcRi/Xro3Fm/79z5/kwhIVavhoYNIUMch/+bN/XMw+bsSI0aWkmAnmmcP5+03L5Egt8SEUk0lIFS6przxfEMzp49y48//kjatGmZM2eO1eKkCKUU48ePZ9y4cXTs2JH58+f77I+joxw4cIAsWfSShVLKJ72eOnbsCMCYMTMpWNCzopKsXw8NGkCJEpA9OyQVruvUiQgKFvTHf2B/LnXvjMqaLdb1116Dnj2hcmW4cgXy5tXlefLo88RYvhwGDHiwfN06PfN45JEHrwUGauWSmkhsRrEP7eG0L57D43JPOosJEyZQuLCOqLlu3TryRo86L0Qpxeuvv864cePo1auXURI2KleuzI0bN7hx4wY3b94kIiIi5j3g1V5Pv/zyC5UqVSIoKIi0aQPo2/dFPM1XYdkyeOYZ/f6ZZ/R5QjMe//NnyPFCByT0NsADSgL0zCS+ib9I4qGeLl6Eo0e10orL8uUQnyf8zz/DggXgY3tukyQxr6fk5bv0AS5evMg77+i0AoMGDeLpp5+2WKKU8d577zFlyhSGDBnC559/nio20zmCL/8dunTpwtmzZ8mbtyATJszFlgrbYwgO1j+2R4/qH/HISP3auTOEhMSuG3LuJsU2vESpgD85dzM9N2/G/4RvT65cWgHkzatfc+ZMuO7KldCiBQ+4DF+7Bvv3w+zZscsPH4bXX4d58yCHZ03SXI5Dj5cikh2d8zdm+6pSaoerhLKKSZMmATpcR9NoK5aXMnnyZMaMGUPPnj357LPPfPrHMblcunQpJnFPPHjvFBJi8pD/8stZ0qWzWJh4WL0aOnYE21cN0OfXr+sf9uPHoVQpOL/jOEd/zUCVHH9xZ/FaugWm4Z134MMPISAArl7VnlJxAzg3bQqLFmlj9qJF0KxZwrIsWwa2YNCxWLVKezjZb9Y/d04btz/9VC+ZpTYcyZndD9iBTu/4P9vrONeK5X4iIiKYOnUq/v7+PqEkRo4cSdeuXfnqq69S3T6JpIiMjOTWrVvcvHnzgQMvdvBYZ7PcVqlSxyOVBOgf5xYtYpe1bKmXej7/HIYPh6YN7jKw523+L8sIwn9cSWTJMowcqWcHDRvC00/D88/fn1289hr8YdsSPGQI7Nih7Qs7d+pz0NftvavOnNEG6Tp1HpRxxYoHl50+/ljPhkaP1q6zcT+Dr5NgmPGYCiKHgBrAr0qpKiJSFnhfKdXBHQLGxVmhmDNnhlu39Ps1a6Br1/+4dasOvXq9yOnTo7l+He7ehXr1YNasB+9ftw5efllPnfv1gzfeeLDOzJnaPS9NGt3frFlQvjzcuwcvvgh794Kfn35KiV4nXbBA+4uLQIEC8N13ejrdtSscO6brXL+uPTAOHHiwz08++YThw4fTtWvXVLtPIimqVasWK+OePSKyTyllyc60lIztiIiIGG+uwMA91KvnxZvrwsPJOuYlbvV7xYTlcBIpDTPuyK9ImC1iLCKSTil1VER85r+3eTMMG6a4dase8C///fcGw4ffN7bZ3O1jERmpn1Q2boRChbTrXNu2WgnY8+yzMNAW8XjFCnj1Va1gvvzyftuXLumnkz17ICpKK5/Dh7VyGDlS+4KPGwcLF95vd8QIyJr1Qbm++uorhg8fTseOHY2SSISkHo68kZ07dwKQL19hatXyTiXhf/wIUdlzEpUrDyEfzEj6BoPbcGSafVZEsgHLgI0ishw47Uqh3MWOHXrdsVevH4C/mTVrFhcv+lHIbh9PxYoP3vfbb1CypN7RGRCgd4DG5w+exW7T6O3b9z0wDh/W02fQLnzZsunZhVL6uH1bv964oWcV9igFP/wA3eNkBPn666/p168fTZs25fvvvzdKIhE2b95stQhOJzAwEICPPgr0OAO2I/gHHSZnpwZkH/qc1aIY4iHJXxOlVHvb23EishXICqx1qVRu4O5dvQ65bRvUr6+DpvXq1Qt/f/0j/sQT2jDWp4/+If/vP73EtGaNNmwVtstJX6gQ7N4dfz/TpsHUqXq5acsWXVa5sp5hdO+u10r37dOvNWvCjBlaOWXKpI16tj1TMezcqT06SpW6XzZnzhz69+9PkyZNWLp0Kek8dYHaQ8jhgy4rs2zrozVqxLPo7uH4Bx0mZ+eG4OfH9Xe/sFocQzw4YsyeF/1eKbVdKbWC2JnvEru3uYgcE5ETIhLPKn5MvY4iokTEbXPmtGm1Mpg69TohISE0adKEdOnS0acPHDmi3fW2bYPatbVSKVBAK4nkMmQInDypvTXefVeX9e2rlUv16jqezRNPaDtGeLhWFPv3a8VUqRJMnBi7vQULYs8mFixYQP/+/WnWrBnLli0jY8aMD/snMSQDTxrb586dA6B48XJkyuRd3m32SsLkk/BcHFl6esz+RETSAElmtLfVmwa0AMqjI9GWj6feI8DLQALP5K7Bzw+mT7/CN98cAd5k7NixMdcKFNA/5suXg78//Pln7HsLFtQzgGjOntVlidGtm/b4AN3mxx9rY/Ty5do4Xbr0feN0iRJ6mapLF+0CGE1EhA5O1rWrPl+2bBm9evWifv36LFmyhAxx4xAYXIInje2QkBAK2dZKBw0a61GhOhwh6+jBRkl4AQkqChF5U0RuApVE5IaI3LSdXwIciNBCTeCELfrsPSAQeCaeehOAD4Gw5IufMiZOHAO0Inv2oRw58gSgjc3h4fr6hQvaXzuuEqhRQ/t7nzqll5QCA4kVd2b4cB3E7Pjx+2WrV99fLrpzR9shQBvE/f21IbxgQW2/uHz5/rVy5e63sWkTlC2rZyMbNmygS5cuPP7446xYscIoCffiMWP7gw8+ACBHjjy0bdvRVd24jOBpC7iyeLtREh5OYjuzJwITRWSiUiqebSlJUhCwe+7mLFDLvoKIVAMKK6VWi8jrCTUkIgOAAQBFihR5CFHiQzFz5kzy5MnD3r0FqF9fG5a3bdOeR9GbbSZPhnz5Ytso/P21N1KzZtoDqm9feMw276pUCboeGk0NdlF65DZEoEIF7QNuC+bJpUs6XLG/v7Z12HLeU6AAjB0L9evrpbGiRWHu3PsSBwbqZaddu3bRvn17ypcvz7p162LiFhnchseM7ehYZDt3/kfmzN4R6NE/6DCZ5k4jZPynROXND3nzWy2SIQkcMWa/KSJtgfq2om1KqRQH2LXl3p4K9HZAhlnALNC+5intOyoqipYtu7FoEXTr1o3ChfXsAPTMIL5Nu3FtFPXr6xnC2rV6R2mLFvD119rl9TPurxcppcsOHICMGfXeik8+gb//1tf//hs6ddJeTytXanfafv20EonL3Lmwfft2WrRoS8GCBVm/fj3ZokNaGjwGd43twMBALtumn5kyeY+SiLZJ3Bo8kshCRa0WyeAASSoKEZmInmp/byt6WUSeUEolFRbrHGDnG0QhW1k0jwAVgG228BL5gBUi0lYp5dKgg2+88QaLFi0CdI6G5BIeHjvmzOef69f8tgejPHn0EtGR/7u/B6JKlfjbiojQSgJihyNIk0bvHM2d+37Zpk2baNu2LUWLFvX6gIVejuVje/v27XS3eTXMnr3FK1Kc2iuJq4u2GiXhRThizG4FNFFKfaWU+gpoDrR24L49QCkReVREAoBuwIroi0qpEKVULqVUMaVUMeBXwOVK4p9//mHy5MmATnWa3P0Gc+YQy0/d/oc8mtKlwU+0sggM1EHQ4rJokbZB5Mt3P2mKPZGRWuH8958+//nnn2nbti0lS5Zk+/btFC1qvmQWYunYVkrRwLaV/4MPAmna1PNjXsdVEhEly1otkiEZOBrXJpvd+3j2BD+IUioCGIqODXUE+EEp9ZeIjLctZVnC66/r5eLFixeTM7HQkvFw+7ZeFopm/Xptb1BK/7B/841OkRg3J9ATT9zfTBd9dOqkA4+dP6/tItHlJ05oY/fgwfreggXB3/9XGjZsSeHChZk5cxOPPZaHJ5/UhnSD+7F6bP/0008AZM2anW7dunpUitOEkJDrqEeyGiXhpTgS66k78AGwFRC0reJNpVSg68V7kJTGenrssce4cuUKFy9edPiekJD72a2iSfTPtmmTfm3cONny2bN2LbRs+TvQAMgFbCf2iocOdDZxYvw2DUPy8YZYT0uWLKFjx458+eUmWrZs5AbJHh4JuX4/h0REhBmoFpHSWE9JziiUUguA2sAS4EegjlVKIqUEBQVx+PDhmLXdpPjjD22kjqsk4u6reIDGjVOsJABKlAgiV67mZMqUA9iJvZKIXv766CMdcDA6SUuHDtqG8tdfevf3b7/pGFJ9+ui9G5GR+r67d+Gdd/Quc4N3MdG2C7N06dIWS5I4/kGHyfNUWTJ+Z4uqaZSE1+KIMXuzUqoRdmuwdmVexRBbzOH27dsnWCcyUm/Gu35dKwn73Ljh4Q6O9eidcwlZsB3g7NmzNGnSBBH4/fcNlCxZkNWr9R6KqrZknatXQ+vW+kc/mqVLSTTWz6uvxj6fMOH++48/1jvFDZ5LREQE0bOOQoUKJ1HbOuxtEvdq10/6BoNHk1jO7PRARiCXLXFR9J7PLGg/cq/i77//ZtOmTeTPn5+n4rMeA2FhOsl6njza9hBN7dp6h7TDu16jf223bXsoWc+fP0/Dhg0JDg5m27ZtMU+OcZO0tGqlZwvduunzd97RSWCiw5Enl+HDtXE9uj2D5/H2228D0KRJp1iJdTwJY7j2PRJ7Pn4ReAUogM6THf0zeQPwushd0SlO59rvYItDdN5deyUxePCDgflcydWrV2nSpAnnz59n48aNVKtWLdH6IrFDkB89qvNsZM6sz//6S2/4W7RIG9Bv3tTLTfPn6+xeO3fqneChoTou1fPP6zoNGsQOPGjwDD788EMA3nvva4sliR8JuU7OLk8bJeFjOGLMHqaU+txN8iTJwxqzq1WrxrFjx7gdHTsjDtmz6+Um0FFlly3TM4yHCsQanYUomTOK4OBgGjZsyNGjR1m7di0NG7rX7fHaNR0yJFpRdu2ql6MuXYo/eb0v4unG7IwZM+Ln58+xYzc8Nq5Txu+/5F6tekZJeBDuMGZ7jJJ4WE6cOMH+/fsZNGhQvNdPnbqvJIYP1+v8Sj2kknhIbt26RcuWLTly5AjLly93u5IAnTD+t9/uny9cqHekV6mi7TYnT7pdJIMdR44cITQ0lG7dBnuckvAPOkzAHr1h6M5z/Y2S8DG8Nj9wcujYUQdLaxN3kd9G8eL6dd68+MN3uJrbt2/Tpk0b9uzZw8KFC2mWWEZ4F1O0qFaSZ8/GLldKJ2sSgTJl7ntZ9et335PK4Fpq1dLhpKpUedJiSWITbZPINryPdoE1+ByJRY+ta3v16iw4x48f5+DBg5QqVeoBI/bXX8c2UPfo4aRO339fHw4QFhbGM888w44dO/j2229pFzeru0UULHh/E2BU1P2UrgBBQfffz5mjPcFq1jQKw5XcunWLmzdvAtCqlSOBEdyDveH62twVxgXWR0lsRvGZ7fUXdwjiKrbZ7ARfRieqRhtuf/9dR32NJr4wGw/NE0/oIwlu375N8+bN2bx5M19//TXPPvusE4VwHiI6odKVK9r8UreuLt+w4X6dPXt01F0fTEftEVy4cAGAIUPGu3VJNDGMd1PqITH1Hy4is4CCIvJZ3ItKqZdcJ5bzmD59OgB16ugUkfv3Q1xHIkcSDyWL6GxDiSiLu3fv0qFDB3bu3Mm3335Lz549nSiAa8iZE7ZujV0WGamj4c6bp73Dpk3Tf+MUbCExxEN0TuxHH/UcV7SM384wSiKVkNiMojWwBZ10ZV88h8fz6quvcuDAAcqVK0eAbRdaXCURFORkJQEwerQ+EiA0NJSOHTuyYcMGvvzyS69QEgnh56c38e3bd185VK2qZyF37lgqms/www8/MGbMGABq1/aAfa62aeONcR9zZdVuoyRSAQkqCqXUFVuojrZKqW/iHm6U8aH44IMP+PjjjwFYunQpoJecoomK0uPd3XsFQkJCaNSoEWvWrGHmzJn0tV//8mL8/PRynu33DIBMmWDSJOtk8gWCgoLoast9O2DA2xQpEk+4YjfiH3SYnJ0a4HfxPPj7E1nQWYnEDJ6MI15PV0VkqYhcsh0/ikghl0uWApRSvPmmTsp39epVypQpQ2SkThwE8MILydhl7USuX79O8+bN2bNnD4GBgbz44ovuF8KFiMD48Xo5KjpUyKhRunzgQL0vxZA8XrHt8u/d+3XGjJlgaaTYaJuE/99ByM0b1glicDuOKIqv0XGeCtiOlbYyj2Xt2rUADBs2jBw5cgCxN4zZzBZu5caNGzRr1ox9+/axcOFCunTp4n4h3ISfH0yZom0V0fzf/+k9GatXWyeXN3L48GEARo+ehJ+FzuwPJB0yOa5TFY4MvTxKqa+VUhG2Yy5g7fw3CcaOHQvA//73v5iyv/7Sr6dPJx40zxVcv36dhg0b8vvvv7No0SI6dOjgXgEsokoVvbw3ciQUKQLBwTqI4YoVSd5qQC9Tnj59mpo1G8TMhq3A/8RR492UynFEUVwRkR4iksZ29ACuulqwh+Xq1avs3buXsmXLkj17dkAHugPt1vkQ+euTzyef6AO4c+cObdq04dChQyxZsoRnnnnGDQJ4Fh9+CP/8A19+qUOldOumQ6TYR701PMjLL78MQN26LSzdiR2VLQcR5SoZJZGKcURR9AW6ABeA80AnoI8rhUoJU21bq8eNGwfoSKrROYrcZlitUgWqVCE8PJwuXbrw888/89133yW4Mzw1EL2L++hRrbjbt4e8eY3dIjE22RJg9es3wpL+0/x7Cu7dIypnbq4u2GCURCrGkVhPp5VSbZVSuZVSeZRS7ZRS/7pDuIdh8+bNpEuXji5duhASotfGAb77zqE9cM5h0yYi16+nV69erF69munTp/u0TSI55MkD69bp9yEh0LOn9kAzPMjFixcpV64KWbK434LtH3SYXG1qk/XtYbrA04JLGdyKT+23j4qKYvfu3bRs2RI/v9gD2+Zh6BbUu+9y/OhRAi9e5MMPP2SgffwLA6VLa9vF1KkwYoRO52pFjC1PJiIigoiICIoXf8ztRmx7w/XtAcONkjD4lqI4a4tkV6xYnVjlzZq5LwRNZGQkJ4KCuHDxIuPGjWPkyJHu6dgLGT5cOxd8/DEULqzPDZrouE4FCxZza78mLIchPnwqemx0mIPp03XqxZ9+gnv37i91uJorV67QpUsXzp8/T5HChWOSJRniR0TPJDp00PsuBg0ygQWj2WULA1OoUFH3dRoRQY4X2hslYXgAhxWFiNQWkXUisk1E2jl4T3MROSYiJ0TkjXiuvyoih0XkoIhsFpGH/lZcuXKFUaNG2c50OOa6dSFt2odtMXlcvXqVBg0asGLFCkqUKEHx4sURM2VPkjRptP0oRw6YOROefdbzAwu6elxHRUXRurWOEFu6tBszRvn7E/zZPKMkDA+QWJjxfHGKXgXaAy2BCUk1LCJpgGlAC6A80F1Eysepth+orpSqBCwGHtov6bvvvrO9ew9Ix79uNLffvHmTli1bcuLECdavX0/hQh69cd3jyJABjh/XIT9++AFefBHCw62WKn7cMa7PnTsHQM6cealTp2aKZU4K/6DDZPz6C1CK8Co1jJIwPEBiK/czReR3YJJSKgy4jnaNjULnzU6KmsAJpdTfACISCDwDHI6uoJSyj0X6K/BQGSGUUrwaHTMCbRMoXPhhWko+169fp2nTpvz+++8sWbKEp59+2gVRBn2fHDngxg3tNvvll7Bpk1YeVoasSACXj+uPPvoIgOHDP3C5bc3eJhHa7llU9hyu7dDglSQWFLAd+slolYj0Al4B0gE5gXYOtF0QOGN3ftZWlhAvAGvjuyAiA0Rkr4jsvXz58gPXN27ciFKK9u17Af5uM4pGpy89cOAAixYtom3btvpCmTL6MCQLPz+dfjVvXp2edtgwj1yGctq4hvjH9mef6aj+rVq51qU6ruHaKAlDQiRqo1BKrQSaAVmBpUCQUuozpdSDv9YpwLbbuzowOQE5ZimlqiulqufO/WD0kOiEP0uX6tvbt3emdPETHbtp9+7dLFiwgPb2na5cqQ9DskmfHi5c0GE/Zszw7uizSY1reHBsL1iwAIDHHnucPHlcF7fDeDcZkkNiNoq2IrIVWAf8CXQFnhGRQBEp4UDb5wD7BaBCtrK4/TQG3kKHM092UIc9e/Zw9epVMmWqCeQBoE6dxO9JKWfOnKF27dr89ttvLFy4MCYndwxTpujD8NBMnAjdu8Mbb8D331stTSxcOq7feustAEaO/CRFQiZF2v2/QZo0RkkYHCKxFdB30euxGYD1SqmawAgRKYW2GHdLou09QCkReRT9ReoGxMr1KSJVgf8DmiulLj3MB/g/29br27fnA9od1pXrukePHqVJkybcuHGD1atX07RpU9d1lorx89M5zc+f17nM9++HyZM9Yu+XS8f1qVOnyJo1Ow0aPOkseWNz7x4EBBDatTdhrTqiMj/imn4MPkViS08hQAegIxAz2JVSx5VSSSkJlFIRwFBgPXAE+EEp9ZeIjBcR22I+k4HMwCIROSAiyY4rum3bLuARoASBga51h92+fTu1a9fm7t27bN++3SgJF5MuHdhyTjFlCtjCd1mKK8d1lC2WSaVKdVzysOMfdJg8T5UlYNc2AKMkDA6T2HBsD3QHwonzxOQoSqk1wJo4Ze/YvW/8MO1GExkZycmTx9FOJ64N07Fu3TratWtHsWLFWLt2LY8++qjrOjPEkC0b/PcflCypkyKdPg2zZ7tvp318uGpcR9p2G5Yr5/y9E/5Bh8nZ5WkQITJPfqe3b/BtkkqF+rlSaqZSyiPTWe3evRuIAFq4dEfv4sWLadu2LeXLl2fXrl1GSbiZ/Pl1LovixeGbb6BGDThxwmqpnE+YLZRuyZIVndquvZK4smibSTpkSDZeHcJj+XLtWVSt2pMuC5w2Z84cOnfuTPXq1dm8eXNMxrxEmTdPHwanERCg7RS5csGBAzrX+ZYtVkvlXG7fvg1AgQLOC9uR5ty/RkkYUoxXK4qlS7cBaenf3/mDXynF+PHj6d+/P82bN2fz5s0xiZCSpHBh9+34S0VkyQKXLsGTNjtv+/ax0616O6GhoQBUqVLDaW1G5itIaPvnjJIwpAivVhSnTp0BauHszKJRUVG88sorjB07li5durBkyRIyZMjgeAMLF+rD4HREYOdObau4cQOqVYNp06yWyjn42abFGTKk3CPD//gR/M6dgTRpuDF2ilEShhThtYoiJCSEiIhzQAvy5HFeuxERETz33HN89tlnvPrqqyxYsCB5SgL0TrEZM5wnlOEBihSBP/7Q74cOhY0brZXHGYSFhVG4cPEUhy2J3kyXfagXRFg0eAVeqyhWrlwNQJ06pZ3WZkREBM8//zyBgYFMnDiRjz76yESA9WAqVYLr16FiRWjaFP73P6slShl37twhIiI8RYrC3nAdMvlLj9h4YvB+vFZRTJw4B4AOHVo4pb2bN2/SokUL5s+fz/vvv88bb7xhlIQXkDUrrLE5qo4bB199Zak4KSIqKoqiRUs/tGOGvZIwO64NzsRrFcWRI2eBCnTrlinFbZ08eZKGDRuydetW5syZw5tvvplyAQ1uo1Ah+Ptv7RE1YID7ElW5gtq1mzz0vVnGjzBKwuASvDIVanh4OEoFAW+S0tQPW7dupYPNGr5s2bKYhDEG7+LRR+HkSXjqKejcGbZv14Zub6NEiYc3Ogd/9h1+wVeJLOG85ViDAbx0RrFrl7ZiNm2aMi2xdu1aWrVqRYECBdizZ4/zlMTixfowuJUsWWD1ap3bolUr+OcfqyVKPv7+yfN48g86TLbhfeDuXVSOnEZJGFyCVyqK8ePnAlCnTqOHbuPbb7+lVatWlCpViq1bt1KyZEknSYdeA8mVy3ntGRymQAFYuxbCwqBFC7h2zWqJkkfRoo6Pw2ibRLpt60hz8T8XSmVI7Xiloti7V+eNGTjw4Z6eZsyYQZ8+fXj66af55ZdfyONM/1qAuXP1YbCE8uVh2TJtt2jaVEeg9RZKlnRsTD8QlqOICStjcB1eqSjCw0+SPn1b8uVLnlfS3bt3GT58OIMHD6ZFixasWLGCjBldkBzGKArLeeop+PZb2LdPzzL+/ttqiZLG39+fgICkx7SJ3WRwN16pKEJD/6ZECUdyJ93n2rVrNG/enE8++YSBAweydOlS1ygJg8fQtSu8/bZ+X6IELFlirTxJISIObXuQu2FE5chllITBbXidorh7NxwI5dFHizt8z+7du6lcuTK7du3iu+++Y8aMGaR1ZeIKg8cwYYL2gALo2BF++81aeRIncS3hd+0KAOEVq3F500GjJAxuw+sUxeXLNwF47DHHZhTTp0+nYcOGpE2blp9++onnnnvOleIZPJD69XUe7uLFoXVrzw1RLiIJbrbzDzpM7oaPkWnWx7rAVeGSDYZ48LrRduuWjtnftm2tJOtOnTqVIUOG0KBBA3bt2kWNGs6LymnwLvLm1d5QUVHaG+ryZaslehClVLxLT9Gxm/DzI+zplu4XzJDq8TpFcffuHfz8SvDEEwnnhYiKimLMmDGMGDGCTp06sWLFCvLly+c+IdesuR9XwuAxlC4NK1fC2bPQpg3cuWO1RLHJmPGRBxSFvZIwNgmDVXidooiIuI1SBRO8fuPGDdq3b8+7775L3759WbBgAf7uzpuZMaM+DB5HnTqwYIG2VTz7LC7NjJhclIqKdS63b5GzW2OjJAyW43WKAiIoXrx8vFdWrlzJo48+yqpVq/j000+ZPXu2+5UEwPTp+jB4JO3awWefwfLl8NJLnhOJO23agFjnKlNmboz5yCgJg+V4ZaynevWqxjpXSjF16lRGjRpFxYoVWbNmDbVqJW3DcBk//KBfBw+2TgZDogwdCv/+C5MnQ9GiMHKk1RJBGlt8cf+gw6S5cI679ZsQ2v5Zi6UyGFw8oxCR5iJyTEROiMgb8VxPJyILbdd3i0gxR9qtUOG+a2x4eDgDBgzgtddeo3Xr1mzbts1aJWHwGj74ALp1g1GjYP785N3rirEtIjE2iawjB8C9e8kTymBwES5TFCKSBpgGtADKA91FJO6a0QtAsFKqJPAx8KEjbdeqVRylFIsWLaJChQrMnj2b0aNHs2TJErJmzerMj2HwYfz89Ab6p56C3r1h61bH7nPV2PaPiIgxXF/7bi0EBCR1i8HgFlw5o6gJnFBK/a2UugcEAs/EqfMM8I3t/WKgkTiQLejkyV3UqVOHLl26ICIsX76c9957LybnsMHgKOnS6bhQpUtr24WDuGRs5wm+DH5+Jp+EweNwpY2iIHDG7vwsEHdNKKaOUipCREKAnMCVxBru3bsn+fLl48svv6R3797WGKwNPkO2bNqbuU4duHHDoVtcMrYVcGH+ViKKloVwx+U3GJJCe/c9fMZOr/iFFZEBwADb6V3gzwsXLtC/f3/69+/vTlFykYQSi4XzUqkmr1/nYVW/VvbtVveiOGP7XqHGlY+6s39NZA5IY1FAdqv6Tm39CnA3eQHy7HClojgHFLY7L2Qri6/OWRHxB7ICV+M2pJSaBcwCEJG9SqnqLpE4CazqO7X1a2XfIrLXgWouHNt3Lfo/R1j4f3Z/36mt3+i+H/ZeVy7q7wFKicijIhIAdANWxKmzAnje9r4TsEUpT/FqNxgSxIxtQ6rCZTMK27rsUGA9kAb4Sin1l4iMB/YqpVYAc4B5InICuIb+whkMHo0Z24bUhkttFEqpNcCaOGXv2L0PAzons9lZThDtYbGq79TWr5V9O9Svj41t83/2/X5T1LeY2bDBYDAYEsNsPDAYDAZDonisonBV+A8n9PuqiBwWkYMisllEijqjX0f6tqvXUUSUiDjFe8KRfkWki+1z/yUiyQx48fB9i0gREdkqIvttf/MUJ2QQka9E5JKI/JnAdRGRz2wyHRSRaint065tS8a1g327ZGxbNa4d7dsVY9uKcW1r1zVjWynlcQfaQHgSKA4EAH8A5ePUGQzMtL3vBix0U78NgYy294Oc0a+jfdvqPQLsAH4FqrvpM5cC9gPZbed53Ph/ngUMsr0vD/zjhH7rA9WAPxO43hJYi3Y+rw3s9uZxbeXYtmpcWzm2rRrXrhzbnjqjcFn4j5T2q5TaqpSKTnnzK9qH3hk48pkBJqDjBoW5sd/+wDSlVDCAUuqSG/tWQBbb+6zAfyntVCm1A+2JlBDPAN8qza9ANhHJn9J+sW5cO9S3i8a2VePa0b5dMbYtGdfgurHtqYoivhAJcbMVxQqRAESHSHB1v/a8gNbOziDJvm3TxMJKqdVO6tOhfoHSQGkR+VlEfhWR5m7sexzQQ0TOor2Mhjmp75TK5ap2XTGuHe3bHmeNbavGtUN945qx7anjGh5ybHtFCA9PRER6ANWBp9zUnx8wFejtjv7i4I+eojdAP2XuEJGKSqnrbui7OzBXKTVFROqg9yZUUHHTwRmchjvHtsXjGqwb2141rj11RpGcEAlIIiESXNAvItIYeAtoq5S6m8I+He37EaACsE1E/kGvL65wguHPkc98FlihlApXSp0CgtBfrpTiSN8vAD8AKKV+AdKj40C5EofGgYvadcW4drRvV4xtq8a1I32Da8a2p45rR2V7EGcYUJx9oLX838Cj3DcGPRanzhBiG/1+cFO/VdGGqlLu/sxx6m/DOcZsRz5zc+Ab2/tc6KlrTjf1vRbobXtfDr2WK07ouxgJG/xaEdvg95s3j2srx7ZV49rKsW3luHbV2HbKYHDFgbbOB9kG7lu2svHoJx3QGngRcAL4DSjupn43AReBA7Zjhbs+c5y6zvxCJfWZBb08cBg4BHRz4/+5PPCz7ct2AGjqhD4XAOfRwbzPop/uBgID7T7vNJtMh5z1d7ZyXFs5tq0a11aObSvGtSvHttmZbTAYDIZE8VQbhcFgMBg8BKMoDAaDwZAoRlEYDAaDIVGMojAYDAZDohhFYTAYDIZEMYrCCYhIbhH5SUT+FJF2duXLRaTAQ7S12xZVsp7ThU26/39EJNGNPyIyOs75LtdKZUhtiEikiBywO4qJSAMRCbGdHxGRsba69uVHReQjq+X3NYyicA7dgZnoYGCvAIhIG2C/Uiq5wb4aAYeUUlWVUjudKqXziKUolFJPWCWIwWcJVUpVsTv+sZXvVEpVQYcY6WEXJju6vCrQWkTqul1iH8YoCucQDmQE0gGRttALrwCTErrB9oS0xS72fxERqWK75xnb01GGOPf8IyKTROSQiPwmIiUTastWPldEZorIXhEJEpHWtvLeIvKFXburRKRBPDIuE5F9tjj9A2xlHwAZbPJ9byu7ZXsVEZlsm1kdEpGutvIGIrJNRBbbnvi+d1JEVEMqRSl1G9gHlIxTHorewOaMII4GG0ZROIf56PC9G4H30TkF5qn7IZvj43N06IBKwPfAZ0qpA8A76DwAVWyDPi4hSqmKwBfAJwm1ZVe/GHqm0wqYKSLpk/G5+iqlHkc/vb0kIjmVUm9w/2nvuTj1OwBVgMpAY2CyXQjjqmjlWR4dp9888RkSI/ph5ICILI17UURyokNQ/BWnPDs6VtMO94iZOjCKwgkopUKUUq2UUtWB34E2wGIR+dL2FF0nntvqoBUMwDzgSQe7W2D3Gt1uYm39oJSKUkodR8efKetgP6CVwx/o3ASFSTpY2pPAAqVUpFLqIrAdqGG79ptS6qzS0TEPoBWYwZAQ9ktP7e3K64nIfmAD8IFS6i+78j/QAe7WK6UuuFtgX8aEGXc+Y4D30HaLn9DJZ5YAzZzUvkrgvSP1o88jiP2Q8MAsw7YU1Rioo5S6IyLb4quXDOwjkUZixp7h4diplGqdULmIPAr8KiI/2GboBidgZhRORERKAYWUUtvQNoso9A9zhniq70JHBwV4DnDUcN3V7vUXB9rqLCJ+IlICveRzDPgHqGIrL4xemopLViDYpiTKoqf50YSLSNp47tkJdBWRNCKSG52W8TcHP5fBkGKUDhX+ATDKall8CfNU51zeQ8fyB700tAx4A213iMsw4GsReR24DPRxsI/sInIQ/YTe3YG2/kX/WGdBR5AME5GfgVPoiJlH0MtlcVkHDBSRI2jl8qvdtVnAQRH5PY6dYil6GewPtIIcqZS6YFM0BoO7mAm8JiLF7LylDCnARI/1ImyJXaorpa44WH8usEoptdiVchkMBt/GLD0ZDAaDIVHMjMJgMBgMiWJmFAaDwWBIFKMoDAaDwZAoRlEYDAaDIVGMojAYDAZDohhFYTAYDIZEMYrCYDAYDIny/3xs9UgILea4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scorecardpy as sc\n",
    "train_perf = sc.perf_eva(Y_train, train_class_pred, title=\"train\")\n",
    "test_perf = sc.perf_eva(Y_test, test_class_pred, title=\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gbm1.pkl']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(gbm,'gbm1.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 打分看分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13378684807256236"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.flagy.value_counts()[1]/df.flagy.value_counts()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_p = train_class_pred.copy()\n",
    "test_p = test_class_pred.copy()\n",
    "# whole_p = whole_class_pred.copy()\n",
    "points0 = 45\n",
    "pdo = -23\n",
    "odds0 = 0.133\n",
    "B = pdo / np.log(2)\n",
    "A = points0 + B * np.log(odds0)\n",
    "train_score = np.around(A - B * np.log(train_p/(1 - train_p)))\n",
    "test_score = np.around(A - B * np.log(test_p/(1 - test_p)))\n",
    "# whole_score = np.around(A + B * np.log(whole_p/(1 - whole_p)))\n",
    "\n",
    "arr1 = np.arange(train_score.shape[0])\n",
    "train_score = pd.Series(train_score)\n",
    "s1 = pd.Series(arr1)\n",
    "train_psi = pd.concat([train_score, s1, Y_train.reset_index(drop=True)], ignore_index=True, axis=1)\n",
    "train_psi.columns = ['score', 'id', 'y']\n",
    "\n",
    "arr1 = np.arange(test_score.shape[0])\n",
    "test_score = pd.Series(test_score)\n",
    "s1 = pd.Series(arr1)\n",
    "test_psi = pd.concat([test_score, s1, Y_test.reset_index(drop=True)], ignore_index=True, axis=1)\n",
    "test_psi.columns = ['score', 'id', 'y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.0 6.0\n"
     ]
    }
   ],
   "source": [
    "print(train_score.max(), train_score.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.0 6.0\n"
     ]
    }
   ],
   "source": [
    "print(test_score.max(), test_score.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'pic': {'score': <Figure size 432x288 with 2 Axes>},\n",
       " 'psi':   variable       PSI\n",
       " 0    score  0.004457}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.perf_psi(score = {'train':train_psi[['score']], 'test':test_psi[['score']]},\n",
    "      label = {'train':train_psi['y'], 'test':test_psi['y']},\n",
    "      # x_limits = [300, 1000],\n",
    "      x_tick_break = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average PSI:0.065500\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>y</th>\n",
       "      <th>0_left</th>\n",
       "      <th>1_left</th>\n",
       "      <th>bad_ratio_left</th>\n",
       "      <th>good_ratio_left</th>\n",
       "      <th>0_right</th>\n",
       "      <th>1_right</th>\n",
       "      <th>bad_ratio_right</th>\n",
       "      <th>good_ratio_right</th>\n",
       "      <th>psi_bad</th>\n",
       "      <th>psi_good</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[0, 10)</th>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035525</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028831</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[10, 20)</th>\n",
       "      <td>116.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.087680</td>\n",
       "      <td>255.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.004843</td>\n",
       "      <td>0.082604</td>\n",
       "      <td>0.015167</td>\n",
       "      <td>0.000303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[20, 30)</th>\n",
       "      <td>198.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.149660</td>\n",
       "      <td>498.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.019370</td>\n",
       "      <td>0.161322</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.000875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[30, 40)</th>\n",
       "      <td>334.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.129944</td>\n",
       "      <td>0.252457</td>\n",
       "      <td>776.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.147700</td>\n",
       "      <td>0.251377</td>\n",
       "      <td>0.002274</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[40, 50)</th>\n",
       "      <td>260.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.169492</td>\n",
       "      <td>0.196523</td>\n",
       "      <td>626.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.176755</td>\n",
       "      <td>0.202786</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.000196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[50, 60)</th>\n",
       "      <td>198.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.209040</td>\n",
       "      <td>0.149660</td>\n",
       "      <td>486.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.210654</td>\n",
       "      <td>0.157434</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[60, 70)</th>\n",
       "      <td>105.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>236.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.179177</td>\n",
       "      <td>0.076450</td>\n",
       "      <td>0.026263</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[70, 80)</th>\n",
       "      <td>48.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.036281</td>\n",
       "      <td>93.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.128329</td>\n",
       "      <td>0.030126</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.001144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[80, 90)</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.079096</td>\n",
       "      <td>0.009826</td>\n",
       "      <td>25.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.089588</td>\n",
       "      <td>0.008098</td>\n",
       "      <td>0.001307</td>\n",
       "      <td>0.000334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[90, 100)</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005650</td>\n",
       "      <td>0.003023</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.043584</td>\n",
       "      <td>0.000972</td>\n",
       "      <td>0.077502</td>\n",
       "      <td>0.002329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "y          0_left  1_left  bad_ratio_left  good_ratio_left  0_right  1_right  \\\n",
       "score                                                                          \n",
       "[0, 10)      47.0     0.0        0.000000         0.035525     89.0      0.0   \n",
       "[10, 20)    116.0     3.0        0.016949         0.087680    255.0      2.0   \n",
       "[20, 30)    198.0     3.0        0.016949         0.149660    498.0      8.0   \n",
       "[30, 40)    334.0    23.0        0.129944         0.252457    776.0     61.0   \n",
       "[40, 50)    260.0    30.0        0.169492         0.196523    626.0     73.0   \n",
       "[50, 60)    198.0    37.0        0.209040         0.149660    486.0     87.0   \n",
       "[60, 70)    105.0    45.0        0.254237         0.079365    236.0     74.0   \n",
       "[70, 80)     48.0    21.0        0.118644         0.036281     93.0     53.0   \n",
       "[80, 90)     13.0    14.0        0.079096         0.009826     25.0     37.0   \n",
       "[90, 100)     4.0     1.0        0.005650         0.003023      3.0     18.0   \n",
       "\n",
       "y          bad_ratio_right  good_ratio_right   psi_bad  psi_good  \n",
       "score                                                             \n",
       "[0, 10)           0.000000          0.028831       NaN  0.001398  \n",
       "[10, 20)          0.004843          0.082604  0.015167  0.000303  \n",
       "[20, 30)          0.019370          0.161322  0.000323  0.000875  \n",
       "[30, 40)          0.147700          0.251377  0.002274  0.000005  \n",
       "[40, 50)          0.176755          0.202786  0.000305  0.000196  \n",
       "[50, 60)          0.210654          0.157434  0.000012  0.000394  \n",
       "[60, 70)          0.179177          0.076450  0.026263  0.000109  \n",
       "[70, 80)          0.128329          0.030126  0.000760  0.001144  \n",
       "[80, 90)          0.089588          0.008098  0.001307  0.000334  \n",
       "[90, 100)         0.043584          0.000972  0.077502  0.002329  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psi_stats_score(test_psi, train_psi, non_computed=None, plot_image=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 保存打分结果及y标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score = np.around(A - B * np.log(train_p/(1 - train_p)))\n",
    "test_score = np.around(A - B * np.log(test_p/(1 - test_p)))\n",
    "train_score = pd.DataFrame(train_score,index = X_train.index) # train的score拼接cus_num\n",
    "test_score = pd.DataFrame(test_score,index = X_test.index) # test的score拼接cus_num\n",
    "# train_score['pred'] = train_p\n",
    "# test_score['pred'] = test_p\n",
    "score_total = pd.concat([train_score,test_score],axis = 0) # train和test拼在一起\n",
    "# result = pd.concat([data[['id','cell','user_date','flagy']],score_total],axis = 1) # 三要素和score拼在一起\n",
    "train_result = pd.concat([train_score,Y_train],axis = 1)\n",
    "train_result.rename(columns = {0:'score'},inplace = True)\n",
    "train_result.to_csv('train_score.csv')\n",
    "test_result = pd.concat([test_score,Y_test],axis = 1)\n",
    "test_result.rename(columns = {0:'score'},inplace = True)\n",
    "test_result.to_csv('test_score.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\项目\\\\8.11_建模_借转贷'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 输出报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 1037)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import joblib\n",
    "# bst = joblib.load('gbm1.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bst.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = pd.concat([X_train,Y_train],axis = 1)\n",
    "data_test = pd.concat([X_test,Y_test],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\项目\\\\8.11_建模_借转贷\\\\报告'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('D:\\\\项目\\\\8.11_建模_借转贷\\\\报告')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pd_cell_province</th>\n",
       "      <th>pd_cell_city</th>\n",
       "      <th>pd_cell_type</th>\n",
       "      <th>pd_id_where</th>\n",
       "      <th>pd_id_city</th>\n",
       "      <th>mma_var3</th>\n",
       "      <th>mma_var100</th>\n",
       "      <th>mma_var101</th>\n",
       "      <th>mma_var201</th>\n",
       "      <th>mma_var202</th>\n",
       "      <th>mma_var302</th>\n",
       "      <th>mma_var303</th>\n",
       "      <th>user_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cus_num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>移动</td>\n",
       "      <td>湖北省黄冈市蕲春县</td>\n",
       "      <td>黄冈</td>\n",
       "      <td>M8</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>福建</td>\n",
       "      <td>厦门</td>\n",
       "      <td>移动</td>\n",
       "      <td>福建省泉州市安溪县</td>\n",
       "      <td>泉州</td>\n",
       "      <td>M8</td>\n",
       "      <td>C14</td>\n",
       "      <td>C13</td>\n",
       "      <td>C14</td>\n",
       "      <td>C13</td>\n",
       "      <td>C14</td>\n",
       "      <td>C13</td>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>天津</td>\n",
       "      <td>天津</td>\n",
       "      <td>移动</td>\n",
       "      <td>山东省聊城市莘县</td>\n",
       "      <td>聊城</td>\n",
       "      <td>M8</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>广西</td>\n",
       "      <td>玉林</td>\n",
       "      <td>移动</td>\n",
       "      <td>广西壮族自治区玉林地区博白县</td>\n",
       "      <td>玉林</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>四川</td>\n",
       "      <td>成都</td>\n",
       "      <td>电信</td>\n",
       "      <td>四川省资阳地区资阳市</td>\n",
       "      <td>资阳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>四川</td>\n",
       "      <td>成都</td>\n",
       "      <td>移动</td>\n",
       "      <td>四川省达川地区达县</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M8</td>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>四川</td>\n",
       "      <td>资阳</td>\n",
       "      <td>移动</td>\n",
       "      <td>四川省南充市嘉陵区</td>\n",
       "      <td>南充</td>\n",
       "      <td>M7</td>\n",
       "      <td>C24</td>\n",
       "      <td>C24</td>\n",
       "      <td>C24</td>\n",
       "      <td>C24</td>\n",
       "      <td>C14</td>\n",
       "      <td>C14</td>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>移动</td>\n",
       "      <td>广东省湛江市吴川市</td>\n",
       "      <td>湛江</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>四川</td>\n",
       "      <td>成都</td>\n",
       "      <td>联通</td>\n",
       "      <td>四川省自贡市大安区</td>\n",
       "      <td>自贡</td>\n",
       "      <td>M8</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>C7</td>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>四川</td>\n",
       "      <td>成都</td>\n",
       "      <td>移动</td>\n",
       "      <td>四川省泸州市纳溪区</td>\n",
       "      <td>泸州</td>\n",
       "      <td>M8</td>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        pd_cell_province pd_cell_city pd_cell_type     pd_id_where pd_id_city  \\\n",
       "cus_num                                                                         \n",
       "1                     广东           深圳           移动       湖北省黄冈市蕲春县         黄冈   \n",
       "2                     福建           厦门           移动       福建省泉州市安溪县         泉州   \n",
       "3                     天津           天津           移动        山东省聊城市莘县         聊城   \n",
       "4                     广西           玉林           移动  广西壮族自治区玉林地区博白县         玉林   \n",
       "5                     四川           成都           电信      四川省资阳地区资阳市         资阳   \n",
       "...                  ...          ...          ...             ...        ...   \n",
       "4996                  四川           成都           移动       四川省达川地区达县        NaN   \n",
       "4997                  四川           资阳           移动       四川省南充市嘉陵区         南充   \n",
       "4998                  广东           深圳           移动       广东省湛江市吴川市         湛江   \n",
       "4999                  四川           成都           联通       四川省自贡市大安区         自贡   \n",
       "5000                  四川           成都           移动       四川省泸州市纳溪区         泸州   \n",
       "\n",
       "        mma_var3 mma_var100 mma_var101 mma_var201 mma_var202 mma_var302  \\\n",
       "cus_num                                                                   \n",
       "1             M8         C7         C7         C7         C7         C7   \n",
       "2             M8        C14        C13        C14        C13        C14   \n",
       "3             M8         C7         C7         C7         C7         C7   \n",
       "4            NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "5            NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "4996          M8         C4         C4         C4         C4         C4   \n",
       "4997          M7        C24        C24        C24        C24        C14   \n",
       "4998         NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4999          M8         C7         C7         C7         C7         C7   \n",
       "5000          M8         C4         C4         C4         C4         C4   \n",
       "\n",
       "        mma_var303   user_date  \n",
       "cus_num                         \n",
       "1               C7  2021-03-25  \n",
       "2              C13  2021-03-25  \n",
       "3               C7  2021-03-25  \n",
       "4              NaN  2021-03-25  \n",
       "5              NaN  2021-03-25  \n",
       "...            ...         ...  \n",
       "4996            C4  2021-03-25  \n",
       "4997           C14  2021-03-25  \n",
       "4998           NaN  2021-03-25  \n",
       "4999            C7  2021-03-25  \n",
       "5000            C4  2021-03-25  \n",
       "\n",
       "[5000 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.select_dtypes('O')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] creating woe binning ...\n",
      "Warning: No Grey sample!\n",
      "Warning: No Grey sample!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IFVdcQUhICKGhofTr14+4uDiOHDnChRdeCMAtt9zCggULOHr0aLbLK9J0GJaYjDHGlCqWmIwxpoidd955fP755yQmJhIbG8ucOXOoVq0aNWvW5IcffgBg2rRpXHjhhdSoUSPb5RVpOgzrLm6MqVBKont3ly5d6N+/P1FRUdSvX5/IyEhq1KjBu+++y4gRI4iPj+e0007jnXfeAchxeUWZDsOmvTDGlGulZdqL2NhYqlevTnx8PN27d2fy5MlER0cXybHK+rQXVmIyxphiMHz4cNavX09iYiK33HJLkSWl8sASkzHGFIP333+/pEMoM6zzgzGm3CtrTRanojx81nKVmI4cOcKAAQNo06YNbdu25eeffwZg1KhRtGnTJuMu6iNHjmS7/UsvvUT79u3p0KEDAwcOJDExEYBBgwYRFRXFww8/nLHumDFj+Oyzz/KM6bfffiM6OppOnTrRvn17Jk2alPHeI488QpMmTahevXqmbSZNmkRkZCSdOnXi/PPPZ/369QAkJSUxZMgQIiMj6dixI/Pnz8/Hb8eYiikkJISDBw+WixN2XlSVgwcPEhISUtKhnJJy1fnhlltu4YILLmDYsGEkJSURHx9PeHg48+bN4+KLLyYoKIgHHngAgLFjx2badvfu3RlJoEqVKlx33XX06dOH6OhoXnnlFaZMmULPnj2ZOXMm8fHxDB8+nM8//zzPeJOSklBVKleuTGxsLB06dOCnn36iUaNGLFq0iGbNmtGyZUtiY2Mztjl27BhhYWEAzJ49m4kTJzJ37lwmTJjA0qVLeeedd9i3bx+XXXYZS5YsISCgXF1fGFOokpOTiYmJybjQLO9CQkKIiIggODg403Lr/FACjh49yoIFC5g6dSoAlSpVolKlSgCZulV269aNmTNnZruPlJQUEhISCA4OJj4+nkaNGhEcHExCQgJpaWkkJycTGBjIY489ljFESF7SYwA4ceIEaWlpmWLJTnpSAoiLi0NEAFi/fj0XX3wxAPXq1SM8PJylS5dy9tln+xWLMRVRcHAwLVq0KOkwTD6Um0vt7du3U7duXYYMGULnzp0ZNmwY2ZWs3n77bS677LKTljdu3JiRI0fStGlTGjZsSI0aNbj00ktp27YtdevWJTo6mn79+rFlyxbS0tLy1aNm165dREVF0aRJEx544AEaNWqU5zYTJkzg9NNP5/777+eVV14BoGPHjsyePZuUlBS2b9/OsmXL2LVrl99xGGNMmaCqZepRtWpVzc6SJUs0MDBQFy1apKqqd911lz766KOZ1hkzZoxeeeWVmpaWdtL2hw4d0osuukj37dunSUlJesUVV+i0adNOWq9v3766e/duHTNmjF577bU6efLkbOPJzu7du7VLly76+++/Z1perVq1HLeZPn26Dh48WFVVk5OT9e6779aOHTtq//799bLLLtNPP/3U7+MbYyouIE5LwTncn0e5KTFFREQQERFB165dARgwYADLly/PeH/q1KnMmTOH6dOnZ1SN+frvf/9LixYtqFu3LsHBwVx99dX89FPmCcVmzZrFmWeeSWxsLFu3bmXGjBkZbU7+aNSoER06dMgYasQfN9xwQ0Yni6CgIF566SVWrlzJrFmzOHLkCK1atfJ7X8YYUxaUm8TUoEEDmjRpwqZNmwD49ttvadeuHQBz587lueeeY/bs2VStWjXb7Zs2bcqiRYuIj49HVfn2228z3TmdnJzM+PHjuf/++0lISMhIbqmpqSQlJbF48WIGDx580n5jYmJISEgA4PDhwyxcuJDWrVvn+lk2b96c8fyLL76gZcuWAMTHx2dUT37zzTcEBQVlfEZjjCkvyk3nB4BXX32VQYMGkZSUlGl8qTvvvJMTJ07Qs2dPwHU6mDRpEnv27GHYsGF8+eWXdO3alQEDBhAdHU1QUBCdO3dm+PDhGfueMGECt9xyC1WrViUqKor4+HgiIyPp06cP4eHh7Ny5kypVqpwU04YNG7jvvvsQEVSVkSNHEhkZCcD999/P+++/T3x8PBEREQwbNozHH3+c1157jf/+978EBwdTs2ZN3n33XQD27dtHr169CAgIoHHjxkybNq2of6XGGFPsylV38ZI0atQobr75ZqKioko6FGOMOUlZ6i5uickYYyqAspSYylVVXl4SR95V0iEUSMgLr5R0CMYYU2zKTecHY4wx5YMlJmOMMaWKJSZjjDGliiUmY4wxpUqRJiYR6S0im0Rki4g8mMM614nIehFZJyI2k5YxxlRwRdYrT0QCgQlATyAGWCIis1V1vc86LYGHgPNU9bCI1CuqeIwxxpQNRVliOhvYoqrbVDUJ+BC4Iss6twMTVPUwgKruK8J4jDHGlAFFmZgaA75zMsR4y3y1AlqJyI8iskhEeme3IxEZLiJLRWRpSkpKEYVrjDGmNCjpG2yDgJZADyACWCAikap6xHclVZ0MTAY38kMxx2iMMaYYFWWJaTfQxOd1hLfMVwwwW1WTVXU78CsuURljjKmg8iwxiUgrYBTQzHd9Vb04j02XAC1FpAUuId0A3Jhlnc+AgcA7IlIHV7W3zd/gjTHGlD/+VOX9B5gEvAmk+rtjVU0RkTuBr4FA4G1VXSciTwBLVXW2996lIrLe2/coVT2Y3w9hjDGm/PAnMaWo6usF2bmqfgl8mWXZYz7PFbjXexhjjDF+tTF9LiJ3iEhDEamV/ijyyIwxxlRI/pSYbvF+jvJZpsBphR+OMcaYii7PxKSqLYojEGOMMQb865UXDPwN6O4tmg+8oarJRRhXkWjR/4aSDqFA9pZ0AMYYU4z8qcp7HQgGJnqvb/aWDSuqoIwxxlRc/iSmLqra0ef1dyKyqqgCMsYYU7H50ysvVUROT38hIqeRj/uZjDHGmPzwp8Q0CvheRLYBghsBYkiRRmWMMabC8qdX3rfevEmtvUWbVPVE0YZljDGmosqxKk9ELvZ+Xg1cDpzhPS73lhljjCknROgtwiYRtoiQ7Yzj3nrXiKAinFVUseRWYroQ+A7ol817CnxSJBEZY4wpViJkM+M4s1VZn2W9UOCfwC9FGU+OiUlV/897+oQ3JUUGb8RwY4wx5YM347ib3UEkY8bx9VnWexIYS+aRgAqdP73yPs5m2czCDsQYY0xRqhMkwlKfx3CfN/OccVyEaKCJKl/4czQRIgsaaY4lJhFpA7QHamRpUwoDQgp6QGOMMSXhQIpqwdqFRAgAXgRuzcdmE0WoDEwFpqty1N8Nc2tjag30BcLJ3M50HLg9H8EZY4wp3fKacTwU6ADMFwGgATBbhP6qLM1uh6pcIEJLYCiwTITFwDuqfJNXMLm1Mc0CZonIOar6c147MsYYU2Z5M46T7YzjXmmnTvprEeYDI3NKSj7bbRbhUWAp8ArQWQQBHlbNuQOdPzfYDheRk0pIqjrUj22NMcaUcqqkiJBlxnHWieDNOM7s/O5ThCjcYAyXA98A/VRZLkIj4Gdy6dntT2Ka4/M8BLgK2JPfII0xxpReqmQz4ziP5bBuDz92+SowBVc6SvDZdo9XisqRPyM/ZOqVJyIfAAv9CMoYY0zF9akq03wXiPBPVV7Oujwrf7qLZ9USqFeA7YwxxlQcg7NZdqs/G/ozUeBx3EgP4v38HXggH8EZY4ypIEQYiOs40UIkU9tUKHDIn334U5UXWrDwjDHGVEA/4SbergOM81l+HFjtzw786fyQPpDr+bgS0w+q+lm+wjTGGFMhqPIb8BtwTkH3kWcbk4hMBEYAa4C1wAgRmVDQAxpjjCm/RFznOBGOi3DM53FchGP+7MOfEtPFQFtVVXcweRdYV+CojTHGlFuqnO/9LHAzkD+JaQvQFFc0AzdsxZaCHtAYY0z5JUKt3N5XzbsDRG6DuH6Oa1MKBTaIyGLvdVdgcf5CNcYYU0Es48+e3FkpcFpeO8itxPRCAYMyxhhTQalyyvP15TaI6/9OdefGGGMqFhHaqLLRm7/pJKosz2sfuVXlLVTV831usM14C1BVDct3xMYYY8q7e4HhZL6HKZ3iOtTlKrcSk9ezwm6wNcYY4x9VNzOuKhcVdB+59soTkUBgnaq2KegBjDHGVDwihAB34DM4AzBJlcS8ts31BltVTQU2iUjTwgjUGGNMhfFvoD1u+ovXvOe5jiqezp/7mGoC67zu4nHpC1W1f/7jNMYYU0F0UKWdz+vvRVjvz4b+JKZ/FSwmY4wxFdhyEbqpsghAhK6Q+1Ts6fxJTH1UNdM0FyIyFrDu5MYYYzIRYQ2uTSkY+EmEnd7rZsBGf/bhT2LqycnzL12WzTJjjDGm76nuILf7mP6G61Fxuoj4zqERCvx4qgc2xhhT/njTXmQQoR4Qkp995FZieh/4CngGeNBn+XFV9WsWQmOMMRWTCP1xN9k2AvbhqvI24Hrn5SrH7uKqelRVdwCPAr+r6m9AC+AmEQk/9bCNMcaUY08C3YBfvfHz/gKuI0Re8pwoEPgYSBWRM4DJuGkv3i9goMYYYyqGZFUOAgEiBKjyPXCWPxv60/khTVVTvOnVX1XVV0VkxalEa4wxptw7IkJ13IgP00XYh8+9sLnxp8SULCIDgcHAHG9ZcIHCNMYYU1FcASQAdwNzga1AP3829CcxDQHOAZ5S1e0i0gI/h5UwxhhTMakSB9QF+gCHgBle1V6e8kxMqrpeVe9S1Q+819tVdeypBGyMMaZ8E2EYbrbzq4EBwCIRhvqzbW73Mc1Q1etEJP0u3kxUNSrvwKQ38DIQCExR1WdzWO8aYCbQRVX9GrLCGGNMqTYK6JxeShKhNvAT8HZeG+bW+eGf3s8C3cXrTZkxATdyRAywRERmq+r6LOuFesf6pSDHMcYYUyodBI77vD7uLctTbhMF7vV+/pbTOnk4G9iiqtsARORDXGNY1tFlnwTG4rKrMcaYMkyEe72nW4BfRJiFq3W7Alid44Y+cqvKyzqleiZ+TK3eGNjl8zoG6JrlGNFAE1X9QkRyTEwiMhw3VS+VKlXK47DGGGNKUPqs51u9R7pZ/u4gtxJTKICIPAnsxfXEE2AQ0DC/kWYlIgHAi8Ctea2rqpNxN/dSrVq1HJOlMcaYkqXKaN/X3r1MqBLr7z786S7eX1UnqupxVT2mqq/jimR52Y0bJSJdhLcsXSjQAZgvIjtwQ1fMFhG/7gw2xhhTeonQQYQVwDpgnQjLRPIeJw/8S0xxIjJIRAJFJEBEBuHf3btLgJYi0kJEKgE3ALPT3/TG4qujqs1VtTluDKX+1ivPGGPKhcnAvao0U6UZcB/wpj8b+pOYbgSuA/7wHtd6y3KlqinAncDXuBFlZ6jqOhF5QkRsWnZjjCnfqnnj4wGgynygmj8bimrZarKpVq2axsX5NdzSSRou+KmQoykee7ufW9IhGGPKOBGJV1W/EkPhHI9PgeX8OVLQTcCZqlyV17b+lJiMMcaY/BqKG5LoE9wsFXW8ZXnyZ3RxY4wxxm8iBAKfqHJRQbbPs8TkDdqa5zJjjDEGQJVUIE2EGgXZ3p8S08dAdJZlM4EzC3JAY4wxFUIssEaEb/Dpya3KXXltmNvID21wc7PX8CYJTBcGhBQ8VmOMMRXAJ94j33IrMbXGDeAaTubJnY4DtxfkYMYYYyoGVd4VoRLQBje83SZVkvzZNrchiWYBs0TkHFX9uXBCNcYYUxGI0Ad4AzdengAtRPirKl/lta0/bUxbRORhoLnv+qrqV7c/Y4wxFdKLwEWqbAEQ4XTgCyicxDQL+AH4L5B6CkEaY4ypOI6nJyXPNjLPz5QjfxJTVVV9oEBhGWOMqaiWivAlMAPXxnQtsESEqwFUc+4Y4c/ID3NEpE+hhGmMMaaiCMGNr3oh0APYD1TBdabLdWZ0f0pM/wQeFpETQDKuEUv9mCjQGGNMBaXKkIJum2diSp8w0BhjjCkOeSYmEeme3XJVXVD44RhjjKno/KnKG+XzPAQ4G1gGXFwkERljjKnQ/KnK8x31ARFpAowvqoCMMcaUXSLcm9v7qryY1z4KMu1FDNC2ANsZY4wp/9L7JbQGugCzvdf9gMX+7MCfaS9eFZFXvMdruJttlxcgWGNKvV274KKLoF07aN8eXn7ZLf/Pf9zrgABYujR/2wI88ABERcHgwX8ue+89GD++yD6KMSVCldGqjAYigGhV7lPlPtyMFE392Yc/JSbff8MU4ANV/THf0RpTBgQFwbhxEB0Nx4/DmWdCz57QoQN88gn89a/537ZxY1i+HFavhmHDYM0aOOMMeOcdmDu3+D6bMcWsPmQatDXJW5Ynf9qY3hWRSkArb9GmfIdnTBnRsKF7AISGQtu2sHu3SzAF3bZJE0hOBlWIj4fgYHjhBfjHP9xzY8qpfwOLRfjUe30l8K4/G/pTldcD2AxMACYCv+bUhdyY8mTHDlixArp2PbVtQ0OhTx/o3Nklrho14Jdf4MorCzlgY0oRVZ4ChgKHvccQVZ72Z1t/qvLGAZeq6iYAEWkFfIDNYGvKsdhYuOYa1wYUls8xTrLb9v773QNcdd4TT8CUKTBvnmt7evTRwozemNJBlWUi7MKbXFaEpqrszGs7f8bKC05PSu5A+itgFRCm3EpOdoll0CC4+uq818/PtitWuCq91q1dh4oZM2DrVti8uXBiN6a0EKG/CJuB7cD/vJ95TnkB/iWmpSIyRUR6eI83ydwhwphyQxVuu821D92b690Yf5o+HZo3BxGoWdP9zGnbf/0LnnzSJbBUbxKZgADX9mRMOfMk0A34VZUWwCXAIn829Ccx/Q1YD9zlPdZ7y4wpd378EaZNg+++g06d3OPLL+HTTyEiAn7+GS6/HHr1cuu/9prrAv7bb+51XJyrnmvW7M9t0332GZx1FjRqBOHh7v3ISEhMhI4di/NTGlMsklU5CASIEKDK98BZ/mwoqpr7CiLVgERVTfVeBwKVVbVErvGqVaumcXFxBdq24YKfCjma4rG3+7klHYLJQbNmsDObGvNmzVwHCGNKCxGJV9VqxXc8/ovrifcMUAfYB3RRJc8Tmj8lpm9xc2ikq4KbzdaYCikhAWbNgltuyT4pgStBHTlSrGEZU9pcAcQD9wBzga240R/y5E9iClHV2PQX3vOqBQjSmDLr2DH44AO49lqoW9d19f78c6iWy/Vns2bw8MOwf3+xhWlMgYnQW4RNImwR4cFs3r9XhPUirBbhWxGa5bY/VeJUSVMlBfgCeNWr2suTP4kpTkSi/wxOzgQS/Nm5McVt165dXHTRRbRr14727dvzsu+4QJ5x48YhIhw4cOCk91auXMk555xD+/btadfuQv7618VcfrlLRjfeCJ99tp9WrRbxzTfwxx9w2WWfUrlySqZ9VK0KTz8NvXvDs8+6BHXPPdCjxyDCw8Pp2zfz5J2DBg2idevWdOjQgaFDh5KcnAzAxo0bOeecc6hcuTIvvPBCpm2GDh1KvXr16NChw6n+yoxBhEDcvaqXAe2AgSK0y7LaCuAsVaKAmcBzOeyrmwjzRfhEhM4irAXWAn+I0NufePxJTHcD/xGRH0RkIfARcKc/OzemuAUFBTFu3DjWr1/PokWLmDBhAuvXr894f9euXcybN4+mTbMfsuvo0TAuuWQW9eqtY9Om+UyefDZr16Zyww376dv3GU6cqEvt2v+iS5ejHDiwl8TEt3nrrSCaNXO98Zo1g8mT4aGH4KOPYP16uO46ePVV+OmnaZxzzkri4zOPyjJo0CA2btzImjVrSEhIYMqUKQDUqlWLV155hZEjR54U56233spcG8/IFJ6zgS2qbFMlCfgQVxWXQZXvVUnvW7AINxZedl4Dnsbd7/odMEyVBkB3XHtTnvJMTKq6BGiD64k3Amirqsv82bkxxa1hw4ZER7sCfmhoKG3btmX37t0Z799zzz0899xziEjGss2bYexYN0pDjx6nMWZMPfbvh0ceEVq2vI5vvtnGgw8eICxsLZBGcnIygYGBPPbYY4wePZpBg1xHh7Q093PQoD/jadMGpk51x7jttgC+/bYp8+dPZvBg2LDBrdOnTx9EBBHh7LPPJiYmBoB69erRpUsXgrMZt6h79+7UqlWrkH97pnyrEyTCUp/HcJ83GwO7fF7HeMtychs535MUpMo8Vf4D/K7quoirstHfSP0pMaGqyaq61nsk+7tzY0rSjh07WLFiBV29MYVmzZpF48aNiYrqSFJSW8aOrUpkJLRqBQ8+6O5heuYZ2LgR1q6Fvn0XExi4hjPOOJ22bdtSt25doqOj6devH1u2bCEtLS0jCealRQt4/XWYPn0RzZt/zscfuxHIr70WVq506yQnJzNt2jR69/artsOYfDqQospZPo/JBdmLCDfhun0/n8MqaT7Pszb75N4N3FOQ+ZiMKfViY2O55pprGD9+PGFhYcTGxvPQQ7O55JKJnHEG7N37FS++qFxwgZue4sorwbd2b+/evdx88828++67BAS467fxPnNU9OvXjzfeeIOnnnqKVatW0bNnT26//fY846pbN4l27abwyy9X8vLLropv5kx3b1RAwAt0796dCy64oJB/G8bkaTfQxOd1hLcsExEuAR4BLlTlRA776ijCMUCAKt5zvNch/gTjV4nJmLIkOTmZa665hhtuuJkaNa7m73+H004LZsOGt3j1VWHPnu8RGU6DBtF8+OHv3HVX5qR07NgxLr/8cp566im6det20v5nzZrFmWeeSWxsLFu3bmXGjBnMnDmT+HwM31C3LowZ47qVjxkD330Xz+efP8Tq1S/x3Xeu9GZMMVoCtBShhQiVgBv4c4I/AEToDLwB9FdlX047UiVQlTBVQlUJ8p6nv/ZrODu/EpOINBaRc0Wke/rDn+2MKW7x8cqll77Grl1P8uyzd3PJJa6Np3v3YKZPhyNHKpGQcBFNm85j1apvaNCgQabtk5KSuOqqqxg8eDADBgw4af/JycmMHz+e+++/n4SEhIy2qtTUVJKSkli8eDGDfWcDzEN4ONSvP4XIyH48+2wyGzcKf/kLnHsuzJljCcoUD69L953A18AGYIYq60R4QoT+3mrPA9WB/4iwUiRz4ipMeVblichY4HrcUETe6F4osKCogjImP44dc0P/fPIJzJmTRkLCPQQEHKNGjc9p3vw7xo3rxdVX59xus3TpUiZNmsSUKVOYMWMGCxYs4ODBg0ydOhWAqVOn0qlTJwAmTJjALbfcQtWqVYmKiiI+Pp7IyEj69OlDeHg4O3fupEqVKtke54ILLmDjxo3ExsYSERHBW2+9Ra9evRgxYgTNmjXjgw+6UKdOJaKj/4+1ay+nXz8IDl5LYOA2KlWaw/jx41m/fj1hYWEMHDiQ+fPnc+DAASIiIhg9ejS33XZbYf9qTQWiypfAl1mWPebz/JLiisWfIYk2AVGqmlN9YrGyIYkMwIEDMHu2S0bffANJSVC/vmsruvpq6NEDKlUq/rhGjRrFzTffTFRU1CntJznZDQ77zDPw66+ud99DD8HAgTa5oCmY4h6S6FT4k5i+Aq71Hf2hJFliqrhiYtxAqJ98Av/7n+ue3ayZm2bi6quhWzcIDMx7P4kj7yryWAtLaprw6eZOPL9zCKtXu959DzwAt94KlSuXdHSmLClLicmfNqZ4YKWIvCEir6Q/ijowYwC2bIHnnnNJp0kTNx35H3+4oX6WL4ft22HcODjvPP+SUlkTGKAMaL2ClStdCbFePRgxAk47DV56yY1mbkx540938dlQdI1cxvhShTVrXKnok0/cc3DTRTz9NFx1lavWqmhEoF8/6NsXvv0WnnrKzfn09NNuuKO//91N2W5MeZBnYlLVd4sjEFNxpaXB4sV/JqOtW92J+IIL3PTkV17pquyM+71ccol7/PijS1CPPOJKlf/4B/zzn1CnTklHacypyTExicgMVb1ORNaQzd26qnpqrbumQktJgQULXCL69FPYs8c16v/lL64NpX9/15nB5Oy881xvxOXLXclpzBhXvTdiBNx3HzRsWNIRGlMwuZWY/un97JvLOsb4LTHR9aD75BPXXnLoEFSpApdd5jovXH65u6/H5E90tBs9Yv1614vvpZfczLpDh8L997tp340pS3Ls/KCqe72fv6U/gDhgp/fcmDwdP+5G2b7+ejfaQf/+roTUp49LUAcOwMcfu4FPLSmdmnbt3LTwv/7qpnufMgVatoQhQ9wyY8qKHBOTiHQTkfki8omIdBYRnzk1xEaZNDk6eBDeecc11tetCzfcAPPnu/mM5s6FffvcCfSqq9zcRaZwnX66m3pj61a44w53YdCmjfs7rF5d0tEZk7fcuotnM6eG5mtODRHpLSKbRGSLiGQzI6LcKyLrRWS1iHwrItbEXUbt3g0TJrg2ovr1XTXS6tXwt7+5tqQ9e+CNN6BXr5K58bUiatLEDVC7Y4drt/vyS+jYEa64wnU2Maa0yi0xBanqPFX15tRQb04N9WtODRHJZkZEyWFGRM11RkRTOm3ZAs8/D+ecAxERcOedLgE9+CAsW+ZOiC+95HrXlcd7jMqKevVc29Nvv8Ho0fDDD27uqZ493Y3KNh6fKW1yS0ynOqeGNyOiblPVHGZE1O9V1Z8ZEU0pkH6P0ejR7sq7ZUvXuJ6c7Lotr1/vJr8bM8Y1yPvMxWdKgZo14bHHXIJ67jn3t+zRw104fPWVJShTeuSWmDqKyDEROQ5Eec/TX0f6se9CmxFRRIaLyFIRWZqSkuLHoU1hSUuDX35xVUGtWkFUlEtMNWq40tD27bB0qRuJoW3bko7W+CM0FEaNcn+7V1+FnTtdZ5SzznIdUtLS8t6HMUUpt155gaoapqqhqhrkPU9/XajDSIpIrjMiqupkVT1LVc8KCrK5DYtaSgp8/727YbNpUzcc0Isvukb1N96AvXtdu9Hdd1tX5LKsShVX/bpli+vBd+yYG3cwMtINIGvXgKakFOVEgX7OiCjpMyL2Ly0jmFdEiYnwxReu00KDBnDxxfDWW3D22a4H3b59rkfd8OF242t5U6kS3Habq4Z9/30ICICbbnI9+d58E07Yf6UpZnmOLl7gHYsEAb8Cf8ElpCXAjaq6zmedzrhOD71VdbM/+7XRxQvP8eOubeGTT1yPrePHISzMdfO++mrXg65amRiLOH/K4vegOEeYT0tzN0A/9ZSrpo2IcFV/w4ZZ9/6yrLyNLl4gqprNjIi6TkSeEJFsZkSUlSJig8UWsYMH3Yyu/fu7e4yuvx6++87d4/LVV7B/P7z3nktM5TEpmbwFBLjxCRcvdqXkFi3cGHwtWsDYsa7Kz5iiVGQlpqJiJab827Pnz3mM5s+H1FR3j8vVV7tHeZ0yIidl8XtQ0nNyLVjgSlDz5rnefXfd5R61apVoWCYfrMRkStzWrfDCC3DuudC4sZsWISbGde9essR1GR4/Hrp3r1hJyRRM9+7w9deuFHXhha5nZrNm7vv0++8lHZ0pbywxlROqsHYtPPEEdOoEZ5zh2gUSE+HJJ909Rhs3ulGozzrL7jEyBdOlixvrcPVq1xY5bpyr4vvHP1y3c2MKgyWmMix9HqMHH4TWrV0338cfd/epvPgibNvmpkR49FG7x8gUrshI14Nv40Y3BuKkSe5iaNgw1/3cmFNhiamMSUlx7UR33eWqUrp2/fOqddIk1570ww9uVtMWLUo6WlPetWzpbivYssXdSvDee+4iadAgWLcu7+2NyY4lpjLgxAnXnXvYMDf520UXuftLzjoL/v1vd4/R11/DX//q7kEyprg1a+bmgNqxw01SOGsWdOjgOtcsW1bS0Zmyxnrl5SJuZkMS5tQHhSp9/6DatXszvZ8WG8jRMS1J3VcZUoWq1++hap99pOwM4ciTrSBFCLtvK5U6xKIpcPj+dtR8eiMSkveYL2nxASQtrknigloEL66bcY9R377un713b+vOXVDWK6/oHTwIr7ziHkeOuHviHnnEjctnSob1yisHkrdVJWFOfWpPWk3tt1Zy4ueapMSEZFon/tMGBDVPoM7bq6j18lqOT2yGJgvxsxsQ9o/t1By7gbiP3PCA8bMaENJzf65JKe1YEAlz63L44Tbsu6ILR/6vNSeWhnP99a7EtG+fGyrmmmssKZnSrXZt13Pvt9/cyObLl7uefRde6LqcF/f18NChQ6lXrx4dOnTItPxf//oXUVFRdOrUiUsvvZQ9e/Zku/27775Ly5YtadmyJe+++y4AJ06coHfv3nTo0IGJEydmrDt8+HCWL1/uV1yrV6/mnHPOoX379kRGRpKYmAhAjx49aN26NZ06daJTp07s27cv45jXX389Z5xxBl27dmXHjh35/VWUCZaYcpD6WxWC2x5HQtKQIKjU8RiJC7LctCGQFh+IKqQlBBIQlgKBigQpeiIAPRGABClpxwM58VMtqvTaf/JxDgYT/1l9Dt3bjn1XduHoMy1J/rUaVfv9Qa2X11LvkyW8+aabfrxy5WL68MYUkrAw1zlnxw53e8LWra701LWrq+4rrgFjb731VubOnXvS8lGjRrF69WpWrlxJ3759eeKJJ05a59ChQ4wePZpffvmFxYsXM3r0aA4fPszXX3/N+eefz+rVq5k2bRoAq1atIjU1lejo6DxjSklJ4aabbmLSpEmsW7eO+fPnExz85zCk06dPZ+XKlaxcuZJ69eoB8NZbb1GzZk22bNnCPffcwwMPPFDQX0mpZiOi5iCoRTzHpzQl7WgQUjmNE4tqEtw6NtM6Va/ey5GH2rL/6rPQhEBq/N+vSABUvWovR55uCUkBhI3cSuy/m1DtphjEuwxI2VOZEz/UJnFBLZLXhYIKgREJVLthNyEXHCKoTax15zblStWqbvSIESNcu+izz7rRJSIj3cj0115btPfTde/ePdvSRVhYWMbzuLg4JJt/vK+//pqePXtSy7ubuGfPnsydO5fw8HDi4+NJTk4mvUnkX//6F5MmTfIrpnnz5hEVFUXHjh0BqF27dp7bzJo1i8cffxyAAQMGcOedd6Kq2cZdllmJKQdBzROoduNuDo1sx6FRbQk6Iw4CM9c/JC2uSVDLOOp+spTaU1ZxfHwL0uICCayfRO2X11H79TVI5TRS91cCUQ4M7cgfl3fhwMAzOT6xOZoYQPUhu6g9dQV13ltB6PCdBLe1pGTKr8qV4fbbYdMmNzhwSgoMHOhuZ3j7bUhKKv6YHnnkEZo0acL06dOzLTHt3r2bJk3+HI86IiKC3bt307NnT3bs2EG3bt246667mD17NtHR0TRq1Miv4/7666+ICL169SI6Oprnnss8T+qQIUPo1KkTTz75ZEbi840lKCiIGjVqcPDgwYJ+9FLLElMuql6+jzpvrqb2q+sICE0hKCIx0/sJX9WDKqnsvz6aAzd1JvVgMHH/aQi4OvTkDdU5/FAb9/OfkaRsrUZgo0SCWsZS54Nl1HlrNdVviSG4RYIlI1OhBAW5EczXroWZM6F6dTfCecuWMGECJGSdmrQIPfXUU+zatYtBgwbx2muv+b1dUFAQ77//PitWrODaa69l/Pjx3Hfffdx7770MGDCA2bNzH/ozJSWFhQsXMn36dBYuXMinn37Kt99+C7hqvDVr1vDDDz/www8/ZFQVVhSWmHKRetjV96b+UYnEH2oRcknmNqK0JCF+emPS/ggBFUgOJG5aBAdHtmX/dWdycEQUqbuqEBSRSKXOR6gxZgM1n9pEYP0TBDWyuQSMCQhwnXmWLXPTrkREuDmiTjvNDakVG5v3PgrLoEGD+Pjjj09a3rhxY3bt+nPO05iYGBo3zjzn6cSJExk8eDCLFi2iRo0afPTRR4wbNy7X40VERNC9e3fq1KlD1apV6dOnT0anifT9h4aGcuONN7J48eKTYklJSeHo0aN+VQGWNZaYcnHkX63ZP7gThx9qS9jd2wkITSV+Vn3iZ7kJiVJ2hkBqll9hSgDJS8IJahlLUPN46kxfTq1x6wm7eztx7zbh8ANtqXZD9j1/jKmoRNwsugsXukkq27d3Q2o1a+aG1DpypGiOu3nzn7PtzJo1izZt2py0Tq9evZg3bx6HDx/m8OHDzJs3j169emW8f/jwYebMmcPgwYOJj48nICAAESHBK/Z9+umnPPTQQ9nud82aNcTHx5OSksL//vc/2rVrR0pKCgcOHAAgOTmZOXPmZPQm7N+/f0avwJkzZ3LxxReXu/YlsPuYTsnvPc5xJaWsRGkw/+dCO05Zu4eltLP7mMqGX35xI5p//rkbZuvvf3cjmngd1PJl4MCBzJ8/nwMHDlC/fn0ebd+aWyPbc8PsL9l86DABIjQNC+WVSy6icWh1lv3+B1NWreX1Xn8B4N0163lu8VIAHuh6FoM7tMvY96jvf6DfGS3o3iSCxJQUBnw2hz2xcQyL6sAd0R15aclyUtLSGNX1rJPi+mD9Rp5fvAwBerVoztMXnkdccjI9P/yY5LQ0UlW5qGkTnutxPoEBATDmOW6++WZWrFhBrVq1+PDDDznttNP8+h2UpfuYLDGdgn3XRbtqvCwC6idSb4Z/9zH4oyKelIqSJaayZdUqN/jwf/4DISFu6KORI121X0Eljryr8ALMw5Av5/FcjwuoW7XKKe8r5IVXCrytJaYiVJoSU/w3dTj2/Olwwqefa+VUwkZtpWrPA4V2nIp8UioKlpjKpo0bXTfz995zbVNDhsADD7j2qPwqi98BOLXvQVlKTNbGdAqq9jxA2KitBNRPBFEC6icWelIyxjht2rjZlzdvdj34pk6FVq1g8GDYsKGkozOFyRLTKara8wD1ZiynwfyfqTdjuSUlY4pYixbw+uuwfbu7affjj11niQEDYMWKko7OFAZLTMaYMqlRIzfly2+/udEjvvkGoqPh8svhp7JZU2c8lpiMMWVanTowZoxLUGPGuN58550HF18M335b/APGmlNnickYUy6Eh7upNX77zZWkNm6ESy6Bc8+FOXMsQZUllpiMMeVKtWpw772wbZtri9q7F/r1g86dYcYMSE0t6QhNXiwxGWPKpZAQN5r55s2uB19iIlx/vesokTC3LppS/kZMKC8sMRljyrXgYLjlFli3Dj76yI1wfvSZlhwY1Jn4WfXRE5agShtLTMaYCiEwEK67DlauhPBnNhBQK5ljL57O/oFnEjejIWkJdjosLWyiQGNMhSICIecepvI5h0laXoO4aREcn9CC2PciqHbtHqpe9TsB1a0hqiRZYjLGVEgiUPnMo1Q+8yhJa0OJmxZB7JRmxH3YmKpX7aXagL0EhKeUdJgVkpVdjTEVXqUOx6k5dgO131xFpTOPEvdeBPuvP5NjE5qTesDNy6apcOC2KA4/ePLUGJokHHm8Fftv7MzBEZGk7K0MQNKaUA4M6ciB4VGkxLgBn9OOB3LovnZoWvF9vrLGSkzGGOMJbhVHzSc2kbKjCrHvNSb+44bEf9qAKn32ERCWTFCzBDQ+8KTtEr6oj4SmUPf9FSR8W5vYN5oR/vivxH3UiJpjN5D6e2XiZzUg7O87iJ0WQbWbYhArFuTIfjXGGJNFUPMEwh/dQp1py6nSax8Jc+oRNy2CtGNBpCWcnJgSf6xJlV77AAi58CAnltdAFSRI0RMB6IkAJCiNlN2VSdtXmcqdjxX3RypTrMRkjDE5CGp8ghqjtpG6vxIB1VNJXFALkgM48ngrqt0UQ/AZ8QCkHahMYL0kACQIAqqlokeDqDYohqNPt0QqpVHjkc0cf7051YftLMmPVCZYYjLGmFwk/lSTwAZJ1Lh3GwkLahH7ejNO/BJO4vd1qHzuIardHANAwg+1iP+oEWn7KkOAEv+/WlS/Yh+1X18DQNKqMAJqJ4HCkcdbQZASescOAmsll+THK5UsMRljTC6S14Zy4qea7PslGpICSIsLpPI5hwluEU/cxw058bcoCEkhdkJzSPVaR1KF2NdaEFA1jao9D6AKsf+OIPz/fuXYyy0IHfGba3f6uCGht1sJKitLTMYYk4vQ4TsJHe6Sx4kVYcR/1Iiao38FoOp1e0iY3YDjk5qBZhlBIimQ2DebUrXnARK/rkvlbocJCEtBEwMgQEFc+5M5mf1WjDGmAI6/1YSkFTWodsMeyGHk8rR9ldHEABK+qkfVq34HoNp1ezh8f1uOvdaCqv1/L8aIyw4rMRljjJ8qdz6W0aMu9LZdGcsD6p8g7Y+Qk9YPqHcCCUmj1svrMpZV6nicOlNXFX2wZZiVmIwx5hRVv30nVM4yjFHlVLfc5JuVmIwx5hRV7XkAgNg3m5K2rzIB9U5Q/fadGctN/lhiMsaYQlC15wFLRIXEqvKMMcaUKpaYjDHGlCqWmIwxxpQqlpiMMcaUKpaYjDHGlCqWmIwxxpQqlpiMMcaUKkWamESkt4hsEpEtIvJgNu9XFpGPvPd/EZHmRRmPMcaY7InQW4RNImwRIZvzNZVF+Mh7/xcRmhdVLEWWmEQkEJgAXAa0AwaKSLssq90GHFbVM4CXgLFFFY8xxpjsiZDN+ZocztcU+fm6KEtMZwNbVHWbqiYBHwJXZFnnCuBd7/lM4C8ikmXseGOMMUXMO1+zTZV8nK8pkvN1UQ5J1BjY5fM6Buia0zqqmiIiR4HaQKZxPURkODDce6kiklAkEZ+aICClKHZsmbrMsO+AgVL7PahcRYSlPgsmqzLZe57P8zUpImR7vi4MZWKsPFWdDBm/wFJJRJaq6lklHYcpOfYdMGDfg8JQlFV5u4EmPq8jvGXZriMiQUAN4GARxmSMMeZk+TxfU6Tn66JMTEuAliLSQkQqATcAs7OsMxu4xXs+APhOVXOYC9IYY0wR8c7XtBAhH+frnObuPTVFVpXntRndCXwNBAJvq+o6EXkCWKqqs4G3gGkisgU4hPtllFWluqrRFAv7Dhgog98Dr80oy/madSJ452t8ztcU+flarIBijDGmNLGRH4wxxpQqlpiMMcaUKpaY/CAi/bMbUsmP7X4qiniMMaY8s8TkB1WdrarPFmC7c4siHlM4RKS5iCSIyMps3vPrYkREnheRdSLyfJblbUTkZxE5ISIjc9m+hTdO5BZv3MhK3vI7RWRoAT6WKYCs3wUReVtE9onI2ly2ERF5xfvbrRaR6BzWy/MCVUQu8L5HK0WkiYjMLfCHKQcqfGLyvpAbRWSqiPwqItNF5BIR+VFENovI2SJyq4i85q1/rYisFZFVIrLAW9ZeRBZ7X6rVItLSWx7r/ewhIvNFZKZ3rOnpQy+JSB9v2TLvSz6npH4XFdRWVe2UdWE+LkaGA1GqOirL8kPAXcALeWw/FnjJGy/yMG48MoC3gX/4cXxTeHy/C1OB3nmsfxnQ0nsMB17PbiU/L1AHAc+oaidV3QXsFZHz/Am6PKrwiclzBjAOaOM9bgTOB0YCD2dZ9zGgl6p2BPp7y0YAL3tf6rNww3lk1Rm4GzdA4mnAeSISArwBXKaqZwJ1C+8jmVOR5WJkqnfR8JOIbBORAd7y2UB1YJmIXO+7varuU9UlQHIuxxDgYty4Y+DGIbvS2z4e2CEiZxfyRzN+UNUFuIuL3FwB/FudRUC4iDTMulJeF6giMgy4DnhSRKZ7m32GS1YVkiUmZ7uqrlHVNGAd8K13o+8aOGlo9x+BqSJyO66/P8DPwMMi8gDQTFWzG8tvsarGeMdY6e23DbBNVbd763xQiJ/JFK6GuIuVvsCzAKraH0jwrnI/KsA+awNHVDV9XLUY3Hhk6ZYCFxQ8ZFPEshtfrnEO66Y76QJVVafgbl4dparpyahC/+0tMTknfJ6n+bxOI8tNyKo6AngUNzTHMhGprarv40pPCcCXInJxHsdIzbpfU+p9pqppqroeqF9Mx9wHNCqmY5nikd0FanYq9N/eElM+icjpqvqLqj4G7AeaiMhpuJLPK8AsIMrP3W0CTpM/J0i8Ppd1TRESkb97bYQrRSS7E4LvhUVhDfZ9EFf9k36RknV8shDcxY4pnfwZXy4rfy9QK/Tf3hJT/j0vImu83jo/Aatw9cNrvR49HYB/+7Mjr8rvDmCuiCwDjgNHiyRqkytVneBVyXVS1T0F2YeIXCUiz+TjmAp8jxt3DNw4ZLN8VmkF5NgrzJS42cBgr52oG3BUVfcCiMjGU9x3hf7bV/jqJFXdgUsm6a9vzeG9qd6yq7PZzbPeI+u+q3s/5wPzfZbf6bPa96raxmsInwCZ5ksxZcvpwDEAEWmA+1uGAWkicjfQTlWPiciXwDAvAT4AfCgiY4AVuPHI0p0HPF584Zt0IvIB0AOoIyIxwP+p6lsiMgJAVScBXwJ9gC1APDDE27YOp16qvgj44hT3UWbZWHklTETuwV0pV8KdmG73emSZIuZVoc5R1Q55revn/t4D7lHV/YWwr87Avap686lHZvJSmN8FEekLnOZV7Rd0HwuAK1T18KnGUxZZYjIVlog0wVXHHszuXqaSJCI9gc1eqd0UsdL0XRCRurjeep+VZBwlyRKTMcaYUsU6PxhjjClVLDEZY4wpVSwxGWOMKVUsMRljjClV/h+OMERl7i5PxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2XQ1kqeqIIOfNL6vKM8aYkqtSVXlAT1Xt6vN6logsD1aGjDHGVG+B9MrLEpFjc16IyDHYeCZjjDFBEsgd013AbBHZBAhuBoihQc2VMcaYaqvYNiYAEYkA2nkv16nqwaDmqgjWxmSMMSVXmdqYCq3KE5EzvJ8XAX8DjvMef/O2FUtE+ovIOhHZICL3FLHfxSKiItKjZNk3xhhTFkToL8I6ETaIUER5zcUiqAhBK6+LqsrrC8wCzvPzngJTi0pYREKBF4CzgG3AQhGZoapr8u0XDdwG/FSCfBtjjCkjIvgpr5mhSr7ymqNSXhcamFT1fu/pWFXNM/uDiAQy6PZEYIOqbvKOeRc4H/J+UOBB4DFcW5Yxxpijzyuv8cpryrW8DqRX3od+tn0QwHHNgd98Xm/ztuUSke5AC1X9pKiEROR6EVkkIosyMzMDOLUxxpi8GoaJsMjncb3PmwGU13jlNUWW1z77x5c2p4XeMYlIe6ATUDdfm1IdILK0J/RJPwT4P+Da4vZV1VeAV8B1fjjScxtjTPWzO1O1dO1CIgRcXvuYIEIE8AbwtipJgR5YVBtTO+BcIIa87UwHgOsCSHs70MLndZy3LUc00BmYIyIATYAZIjJQVRcFkL4xxpiyUYLyGsgtrxmoit/yWpXTRGgLDAMWi7AAmKjKV8VlJpApiU5W1R+LS8jPcWHAeuBM3AdcCFyhqqsL2X8OMLK4oGTdxY0xpuSK6i4uQiHlNYWU18wBRhYWlPLtGwpcADwL7MeNh71XtfAOdIEMsL1eRArcIanqsKIOUtVMEbkF+AIIBV5X1dUiMhZYpKozAji3McaYIFMlU4R85TWrRfDKa0pcXovQBTcZw9+Ar4DzVFkiQjPgR4ro2R3IHdPFPi8jgQuBHar6j5JmtCzYHZMxxpTc0R5gK8K3wGvAB6qk5XvvalXeLPTYQGZ+yJughADfqeoppcnskbLAZIwxJVcOgel2Vcbn23abKs8Ud2wg3cXzawvEluI4Y4wx1cc1frZdG8iBgSwUeAA304N4P38HRpUgc8YYY6oJEQYDVwBtRPK0TUUDewNJI5Cl1aNLlz1jjDHV0A/ATqAh8JTP9gPAikASCHR28YuA3rg7pnmqOq2kOS0r1sZkjDElVyVmF88hIhOAG4GVwCrgRhF5IdgZM8YYU/mI8J3384AI+30eB0TYH1AaAXQXXwt0UG9Hr1fealXtcIT5LxW7YzLGmJKrTHdMgQyw3QC0BH71XrfwthljjDF5iFC/qPdVi+8AUdRCgR+LyAxcT4qfRWSOiMwGfva2HRUiEikiC0RkuYiszsjIyH1v+PDhdO3alS5dujBo0CCSk5MLTWfr1q3Url2bJ598EoA///yT3r1707lzZ6ZNm5a73/nnn8+OHTuKzdfatWs5+eSTiYiIyE0zR2JiIoMGDaJ9+/Z06NCBH390MzpddtllJCQkkJCQQOvWrUlISABgz549nH766dSuXZtbbrkl0EtjjDEV0WJgkfcz/yOgeVCLumN6soj3jqaDwBmqmiwi4VlZWYfmz5/PSSedxNNPP02dOnUAuPPOO3n++ee55x7/Cy/eeeednHPOObmv33nnHW688UYuuugiBgwYwAUXXMDHH39Mt27daNasWbGZql+/Ps8++2yeoJbjtttuo3///nzwwQccOnSI1NRUAN57773cff75z39St25dACIjI3nwwQdZtWoVq1atCvS6GGNMhaNKIOv1FamohQK/PdLEy4LXtpVzKxSuqnizkecGJVUlLS0td3t+06ZNo02bNtSqdbh6NTw8nNTUVA4ePEhoaCiZmZmMHz+ejz/+OKB8xcbGEhsbyyef5F2aJCkpiblz5/LGG28AUKNGDWrUqJH/MzFlyhRmzZoFQK1atejduzcbNlgNqTGmchOhvSprvfWbClBlSXFpFFWV5/WskAMist/ncUBEAupZUVZEJFRElgG7QkND6dWrV+57Q4cOpUmTJqxdu5Zbb721wLHJyck89thj3H///Xm2X3HFFUyfPp2zzjqLe++9lwkTJnD11VcTFRV1RHndvHkzjRo1YujQoXTr1o0RI0aQv7PGvHnzaNy4MW3btj2icxljTAV0p/fzKT+PgGriCg1Mqtrb+xmtqnV8HtGqWufI8l0yqpqlqglAXHZ2dp7qrokTJ7Jjxw46dOiQp6osx5gxY7jjjjuoXbt2nu1169blk08+YdGiRXTv3p2PP/6YQYMGcd111zFo0KDcdqGSyszMZMmSJdx0000sXbqUWrVqMW7cuDz7vPPOOwwePLhU6RtjTEWm6lbGVeV0P48zAkmjyHFM3p3K2rLIbFlQ1cTQ0FA+//zzPNtDQ0O5/PLL+fDDgqvA//TTT9x99920bt2a8ePH88gjj/D888/n2efBBx/kvvvu45133qF3795MmjSJMWPGlCqPcXFxxMXF5d7VDRo0iCVLDt+5ZmZmMnXqVC677LJSpW+MMZWBCJEi3CnCVBE+FOF2kcBWPy+yu7iqZonIOhFpqapbyya7JSMijYAMVU0UkZohISG0b98eVWXjxo0cd9xxqCozZsygffv2BY6fN29e7vMxY8YU6Pn2yy+/sG3bNv7yl7+wfPlyIiMjERHS0tws7TlBLNDeck2aNKFFixasW7eOdu3a8c0339CxY8fc97/++mvat29PXFxcaS6HMcZUFv/DTUP0nPf6CuBN4JLiDgxkHFM9YLWILAByG0tUdWDJ81kqTYFJIhIKhISGhnLuueeSnZ3NkCFD2L9/P6pK165defHFFwGYMWMGixYtYuzYscUmft999/Hwww8DMHjwYC644ALGjRuXe+zatWs59dRTCxz3+++/06NHD/bv309ISAjjx49nzZo11KlTh+eee44rr7ySQ4cOccwxxzBx4sTc4959912/1XitW7dm//79HDp0iGnTpvHll1/mCWjGGFPJdFbFtxCbLcKaQA4MZOaHvv62l1evvaM988O5557L1KlTC/SsM8aYyqQc1mN6C3helfne617A31X9LoeR99gAAtNjqjqquG1Hi01JZIwxJXe0ApMIK3ETfocD7YCt3utWwNp8d1H+0wggMC1R1e75tq1Q1S6lzfiROJLAlD4y+KvBRz75bNDPYYwxJXUUA1Orot5XzZ3erlCFtjGJyE3AzcCxIuK7hkY08H2gmTTGGFN95A88IsRCYL3xchTV+WEy8BnwKOA7z88BVQ1oFUJjjDHVkwgDcYNqmwG7cFV5PwOdiju2qAG2Saq6Bfg38Luq/gq0Aa4SkZgjz7Yxxpgq7EHgJGC9N3/emeA6QhSn2IUCgQ+BLBE5DngFt+zF5FJm1BhjTPWQocoeIESEEFVmAz0COTCQcUzZqprpLa/+nKo+JyJLjyS3xhhjqrxEEWoD84C3RdiFz1jYogRyx5QhIoOBa4CZ3rbwUmXTGGNMdXE+kAbcDnwObATOC+TAQALTUOBk4GFV3SwibXDTShhjjDF+qZICNAIGAHuBKV7VXrGKHcdU0RzJOKamc38o49wUtLPPKUE/hzHGlFQ5zPwwAvgPMAsQoC8wVpXXizu2qHFMU1T1UhHJGcWbR3kNsDXGGFMp3AV0y7lLEqEB8AMcQWACbvN+nnvE2TPGGFPd7MHNLp7jgLetWEUtrb7T+1ns9BHGGGMMgEjuCrYbgJ9EmI6rdTsfWFHogT6Kqso7gJ8qvBxHexVbY4wxlUK093Oj98gxPdAEirpjigYQkQeBnbieeAJciVsjyRhjjMlDlQd8X3tjmVAlOdA0AukuPlBVJ6jqAVXdr6ov4m7JjDHGGL9E6CzCUmA1sFqExSLFz5MHgQWmFBG5UkRCRSRERK4kwNG7xhhjqq1XgDtVaaVKK+CfwKuBHBhIYLoCuBT4w3tc4m0zxhhjClPLmx8PAFXmAAGNoyp2rjxvhnGrujPGGFMSm0QYzeGZgq4CNgVyYCB3TMYYY0xJDcNNSTQVt0pFQ29bsQKZXdwYY4wJmAihwFRVTi/N8cXeMXmTtha7zRhjjAFQJQvIFqFuaY4P5I7pQ6B7vm0fACeU5oTGGGOqhWRgpQhf4dOTW5V/FHdgUTM/tMetzV7XWyQwRx0gsvR5NcYYUw1M9R4lVtQdUzvcBK4x5F3c6QBwXWlOZowxpnpQZZIINYD2uOnt1qlyKJBji5qSaDowXUROVtUfyyarxhhjqgMRBgAv4+bLE6CNCDeo8llxxwbSXXyDiNwrIq+IyOs5jyPMszEV0m+/wemnQ8eO0KkTPPNM3vefegpEYPdu/8dPmgRt27rHpElu28GD0L8/dO4MEyYc3vf662HJkuB8DmMqgP8DTlflL6r0BU4Hng7kwEA6P0wH5gFfA1mlzqIxlUBYmAs+3bvDgQNwwglw1lkuUP32G3z5JbRs6f/YvXvhgQdg0SIXvE44AQYOhHnzoHdvuPdeOPVUuPlmWL4csrLceYypog6ossHn9Sbyrs9UqEACU5SqjipVtoypZJo2dQ+A6Gjo0AG2b3eB6Y474PHH4fxC5kH54gsXxOrXd6/POgs+/xxiYiA1FTIyQL2FZEaPhpdeCvrHMaY8LRLhU2AKro3pEmChCBcBqBbeMSKQqryZIjKgTLJpTCWyZQssXQq9esH06dC8OXTtWvj+27dDixaHX8fFuW1nneXSOukk+Mc/YMYMd6fUrFmwP4Ex5SoSN79qX+AvwJ9ATVxnuiJXRg/kjuk24F4ROQhk4Bqx1BYKNFVZcjJcfDGMH++q9x55xFXjlUZYGEye7J5nZMDZZ7tAd+edsHUrXHONq/IzpipRZWhpjy32jklVo1U1RFVrqmod77UFJVNlZWS4oHTllXDRRbBxI2ze7O6WWreGbdvcHc/vv+c9rnlz1w6VY9s2t83XhAkuEM2fD3XrwnvvuTYtY8xhxd4xiUgff9tVdW7ZZ8eY8qUKw4e7tqU773Tb4uNh167D+7Ru7To4NGzoXr/9Ntx3H/z6K4SGuk4Pl1zi7rAeffTwcfv2wcyZri3q448hJMR1kkhLO2ofz5hKIZA2prt8HqOBj4ExQcyTMeXm++/hzTdh1ixISHCPTz8tfP8HH4QhQ1xQAtfT7sYboX17+M9/DneEABg71gWwkBBXnTdvngt6V18dzE9kTOUjmtNNKNADRFoA41X14uBkqWi1atXSlJTSLaDbdO4PZZybgnb2OSXo5zAVR+vWh4OSr1atXIcHYyoKEUlV1YAW6juy83BnUe+r8n/FpVGa9Zi2AR1KcZwxVc7Wrf63//or/PTT4e7hxlQj0d6jB3AT0Nx73EjBCcH9CqSN6TlcH3RwgSwBsPHqptpLTITISP9tRCKue3j37vD3v8Pll0NU1FHPojFHnSoPAIgwF+iu6gbVijAG+CSQNAK5Y1oELPYePwKjVPWq0mTYmKpixQro0QPS0yE8PO97UVHw6quuB97Bg64zRfPmrjPFL7+UT36NKQeNIc+krYe8bcUKpLv4JOAdXGBaDiwoRQaNqTLeesvdDaWmwnffwcSJrk1JxP185RUXjG66CVauhG+/dZ0dnnsOjj/ePZ8xw3WUMKYK+x+wQIQx3t3ST8CkQA4stvODiPzFS2wLbnBtC2BIeXUXt84PprwcOuTuel54Afr0cWOQmjQJ/PidO+G11+Dll92MEC1bwg03wIgREBsbvHwbA0ev80Pec3IC0Nt7OVeVpQEdF0BgWgxcoarrvNfHA++oarmsYGuByZSH7dvd2KQff3TBady4glV4gcrMdHdMEybAN9+4dC65xE3uesop7s7LmLJWHoHJnZdYfBaXVaWQLkOHBdLGFJ4TlFyiuh4o5b+kMZXPnDmuE8OKFYdnaihtUAI3RdFFF8HXX8PPP7sqv5kz3Qzk3bq5qsDk5DLLvjHlQoSBIvwCbAa+9X4WuxYTBNj5QUReE5G/eI9XcR0ijKnSVF0Q+utfoV49WLAALr20bM/Rvr1b82nHDlfFp+qq95o3h9tug7Vry/Z8OYYNG0ZsbCydO3fOs3358uWcfPLJxMfHc95557F//36/xycmJjJo0CDat29Phw4d+PFHt5boqFGj6NKlC9dcc03uvm+99Rbjx48PKF/PPPMMnTt3plOnTnmOueyyy0hISCAhIYHWrVuTkJAAQEZGBkOGDCE+Pp4OHTrwqO9UG6a8PQicBKxXpQ3wV2B+IAcGEphuAtYA//Aea7xtxlRZBw64IDRypFvmYsECt/RFsNSq5RYOXLbMzT5x7rnw4otuaqQzz4SpU10VYFm59tpr+fzzzwtsHzFiBOPGjWPlypVceOGFPPHEE36Pv+222+jfvz9r165l+fLldOjQgaSkJJYsWcKKFSuoUaMGK1euJC0tjYkTJ/L3v/+92DytWrWKV199lQULFrB8+XJmzpzJhg1uOZ/33nuPZcuWsWzZMi6++GIuuugiAN5//30OHjzIypUrWbx4MS+//DJbbGRzRZGhyh4gRIQQVWbjxjYVK5DAFAY8o6oXqepFwLNAaOnzakzFtnYtnHiiCwaPPw4ffAB1jtK0xSKunentt90ksI88Ahs2uEllW7d2UyDt3Hnk5+nTpw/1fedL8qxfv54+fdz0mGeddRYffvhhgX2SkpKYO3cuw4cPB6BGjRrExMQQEhJCRkYGqkpqairh4eE8+eST3HrrrYQHUPf5888/06tXL6KioggLC6Nv375MnZp3yR5VZcqUKQwePBgAESElJYXMzEzS0tKoUaMGdY7WL8sUJ1GE2sBc4G0RngEC6iAQSGD6BreGRo6auNVsjalyPvwQevaEPXvgq6/grrvKrzNCbCz861+waZNbJqNzZzf/XsuWcNllMHdu2c8s0alTJ6ZPnw64u5HffKdL92zevJlGjRoxdOhQunXrxogRI0hJSSE6OpoBAwbQrVs3mjZtSt26dfnpp5+44IILAjp3586dmTdvHnv27CE1NZVPP/20wPnnzZtH48aNadu2LQCDBg2iVq1aNG3alJYtWzJy5Ei/AdeUi/OBVOAO4HNgI24tpmIFEpgiVTW3KdZ7HtAYdhHpLyLrRGSDiNzj5/07RWSNiKwQkW9EpFUg6ZqCCmszWLZsGSeddBIJCQn06NGDBQsKDkObPXt2bv19QkICkZGRTJs2DYArr7ySLl26cO+99+bu/9BDD+W+X5xRo0bRuXNnOnfuzHvvvZe7ffjw4XTt2pUuXbowaNAgkr3W/jfeeINGjRrl5uW1114r4ZUoncxMuPtuGDQIOnWCxYvhjDOOyqmLFRrq1mv6/HNYv94tNvjll9C3r5sEdsIEV/VYFl5//XUmTJjACSecwIEDB6hRo0aBfTIzM1myZAk33XQTS5cupVatWowbNw6Au+++m2XLlvHUU08xevRoxo4dy2uvvcall17KQw89VOS5O3TowKhRo+jXrx/9+/cnISGB0NC8lTPvvPNO7t0SwIIFCwgNDWXHjh1s3ryZp556ik2bNpXBlah+ROgvwjoRNojgp7zmThHWiLBChG9EKLK8ViVFlWxVMnEzPjznVe0VK5DAlCIiufMbicgJQLET9YtIKPACcA7QERgsIvlr6ZcCPVS1C/AB8HggmTYFFdZmcPfdd3P//fezbNkyxo4dy913311gn9NPPz23/n7WrFlERUXRr18/VqxYQc2aNVmxYgULFy4kKSmJnTt3Bvwt+JNPPmHJkiUsW7aMn376iSeffDK3Mf3pp59m+fLlrFixgpYtW/L888/nHnfZZZfl5mfEiBGlvygB+uMPt8rsE0+4HnLffpt3JdqKpG1b1yFj+3b4738hIsJNedSsmfu5evWRpd++fXu+/PJLFi9ezODBgzn22GML7BMXF0dcXBy9evUC3F3LkiV5ZylbunQpqkq7du14//33mTJlChs3buSXYqa+GD58OIsXL2bu3LnUq1eP448/Pve9zMxMpk6dymWXXZa7bfLkyfTv35/w8HBiY2M59dRTWbTI+maVlAh+ymsKKa8psrwW4SQR5ogwVYRuIqwCVgF/iNA/kPwEEphuB94XkXki8h3wHnBLAMedCGxQ1U2qegh4F3drl0tVZ6tqqvdyPhAXSKZNQYW1GYhIbjBISkqiWTHreX/wwQecc845REVFER4eTlpaGtnZ2WRkZBAaGsp//vMfHnjggYDytGbNGvr06UNYWBi1atWiS5cuucEzpx1AVUlLS0PKqb5s/ny3ftL8+fDGG+7uIyKiXLJSIlFRMGyYWxdq/nzX/fy//3XVfX/5C0yZ4hY8LKld3sJT2dnZPPTQQ9x4440F9mnSpAktWrRg3To3iuSbb76hY76eIaNHj+bBBx8kIyODLG+Ki5CQEFJTU9m+fTtnnnlmkeffunUrU6dO5Yorrsh97+uvv6Z9+/bExR0uJlq2bMmsWbMASElJYf78+bRv377kH9x45TWbVCmkvGa2KoGU188Dj+BmDJoFjFClCdAHCKjbZCBTEi0E2uN64t0IdFDVxQGk3RzwrSDe5m0rzHAK6eMuIteLyCIRWZRZll2TqoHx48dz11130aJFC0aOHFlsd9p33303t6qkQ4cONGrUiO7du3PeeeexYcMGsrOz6d49oAmC6dq1K59//jmpqans3r2b2bNn52kzGDp0KE2aNGHt2rXceuutuds//PDD3Co+f20cZUHVBaE+faBGDTdwdsiQoJwqqESgVy+YNMl1lnj8cTfj+WWXubao++932/MbPHgwJ598MuvWrSMuLo7//ve/gKsqO/7442nfvj3NmjVj6FC3OvaOHTsYMGBA7vHPPfdcbjXvsmXL8lT1Tps2jR49etCsWTNiYmJISEggPj6e9PR0unbtys6dOwkL8z9/9MUXX0zHjh0577zzeOGFF4iJicl9z/dvM8ff//53kpOT6dSpEz179mTo0KF06dKltJezimsYJsIin8f1Pm+WWXkNhKnypSrvA7+rui7iqgQ8+KHE6zEFnLDIIKC/qo7wXl8N9FLVAndbInIV7i6sr6oeLCpdm/mhcFu2bOHcc89l1apVudv+8Y9/0LdvXy6++GKmTJnCK6+8wtdf+++7snPnTrp06cKOHTv89qI677zzePnll5k4cSLLly/nrLPO4rrrrisyTw8//DDvv/8+jRo1IjY2lp49e3L77bfnvp+VlcWtt96aW6js2bOH2rVrExERwcsvv8x7772X+424rKSmusX83nwTBgxwc9/Vq1empyhXWVluldwJE9wihyEhrsv73/8Op59e/jNLPP/887Rs2ZKBAweWb0aqmaJmfhDBK6/xymu88rpg7ZgIPuU1BcprEZaouuUtfJ/7e11oXoMYmE4Gxqjq2d7rfwGo6qP59vsr8BwuKO0qkFA+FpgK5y8w1a1bl8TEREQEVaVu3bqFDpp85plnWL16Na+88kqB96ZPn87SpUu54oorGDduHK+//jpnn302H330EVEBrudwxRVXcNVVV+X55g0wd+5cHn/8cWbOnJlne1ZWFvXr1ycpKSmg9AOxcaPrer1ihbubGD3aFdxV1aZNbuDuf//rehq2b+/a0YYMgbp1S5dm+sh/lG0m/Yh88tmgn8OfYcOGMXPmTGJjY/P8H1122WW5VZeJiYnExMSwbNmygI8fNWoUn332GQkJCfzvf/8D3MDj3bt35/miVpinn36a1157DREhPj6eiRMnEhkZybXXXsu3335LXe+X+cYbb5CQkMATTzzB22+/Dbi2uZ9//pns7Ow0VfX7zyqCV17jldd45TX5ymt8ymv8ltciZOG6hQuuF3dO9Z8AkarFzxwUzH/JhUBbEWkjIjWAy4EZvjuISDfgZWBgIEHJlFyzZs349ttvAZg1a1ZuN1t/8vd4ypGRkcH48eO5++6787QHZWVlcejQIRYsWJBnpH+OrKws9uxxnXBWrFjBihUr6NevH6qaO3BSVZkxY0Zuu8BOn0E6M2bMoEOHsluT8pNP3FIVW7e65/ffX7WDEsAxx8Bjj7nqvEmTXDC67TbXWeKGG2D58vLOYcVSWCeiwgb4BnL8kQ483r59O88++yyLFi1i1apVZGVl8e677+a+/8QTT+TmLWdGjLvuuit326OPPkrfvn3h8Lp6/njlNW1EKKS8xqe89h+UAFQJVaWOKtGqhHnPc14HNJlXsQsFugxJc6CV7/7FzS6uqpkicgvwBW5A7uuqulpExgKLVHUG8ARQG9e5AmCrqtr9fSkMHjyYOXPmsHv3buLi4njggQcYPnw4r776KrfddhuZmZlERkbm3g0tWrSIl156Kbc79pYtW/jtt99y/oDzeOGFFxgyZAhRUVF06dKF1NRU4uPjGTBgADExMWzdupWaNWsWOC4jI4PTTjsNcJ0d3nrrLcLCwsjOzmbIkCHs378fVaVr1668+OKLADz77LPMmDGDsLAw6tevzxtvvHHE1yYrC8aOdY+EBDdW6ZhjjjjZSiUyEq65xj0WL3bVfP/7n5uX79RT3QSyF19cOTp+BFOfPn2KnDkiZ4BvYdXL/o4/0oHHQO4A4vDwcFJTU4vtxOQr5wvn7Nmzi/hcZIqQr7xmtQheeU2+8hqAraoEpbwOZHbxx4DLcFMR5awgo+UVQKwqr+wdadXMv779jis6tie+UcNC9ymvqpm9e+HKK90YoCFD3DQ/fmJotbR3r+uJ+OKLbnaJ2Fi3BMcNN7iOE4WpylV54L9KPMfcuXO58847i+yS7u/4xx9/nMmTJ3PmmWcycuRIrrvuugJV10V55plnuO+++6hZsyb9+vXLraa79tpr+fHHH4mIiODMM89k3LhxRPh8u0hNTSUuLo4NGzbQoEGDcpldvDQCqci4AGinqgNU9TzvYXc1JtejfXsXGZTKy5Ilriv4N9/ASy+5Bf0sKB1Wv75bwmPdOtdZ4qST3HIebdrABRe4QbzZ2eWdy4qlsOru4hzJwON9+/Yxffp0Nm/ezI4dO0hJSeGtt94C4NFHH2Xt2rUsXLiQvXv38thjj+U59uOPP+bUU0+tdLNhBBKYNmHLXJhKZuJEV0WVmQnz5rm7gPLujVZRhYRAv35u2qNNm2DUKPjhB7fSbvv28PTTsG+fGzcVGwsnvJF3UoAJS/rQ9fX76P7Gv7j3W//fWb/c3IEur99Hp/+O5omf/pq7/dpPrqHnpFH8Z965udvGze9HgBOLHFX+BviWVGkGHn/99de0adOGRo0aER4ezkUXXcQPP7jan6ZNmyIiREREMHTo0AIzu/jrYl8ZBBKYUoFlIvKyiDyb8wh2xowpjYMHXRAaNsxNhrp4sRvnYwLTqpWbOPa339xEso0aubuq5s3dDBlPP513/2+3tmXmxngWXPMYS659lNt7Fmx7ycoWbv/mEqZf9BJLr32E99edwM97mrDyz2bUDMtg4ZDHWPx7S5IORrIzuQ4Ld7YmwOn1jip/A3xLqjQDj1u2bMn8+fNJTU1FVfnmm29yOwXldBZSVaZNm5ZnSrKkpCS+/fZbzj///AJpVnSBBKYZuHU1fgAW+zyMqVC2boXTTnMN+qNGueopW7K8dCIi4Ior3BIcS5fC1Ve7BROvugo2JzXk7dU9Sc8M45XlvRl54ldEhLmB77FRBVc4XPh7K46N+ZM2MXuoEZrFJe2WMHNDPOEhWaRlhpOtQkZ2KKGSzYM/DODfp3x6lD/tYYUNPAb/dx/5Bx4XdXxpBx736tWLQYMG0b17d+Lj48nOzub6693Y2CuvvJL4+Hji4+PZvXs3//73v3OP++ijj+jXrx+1alWKZqU8gjaOKVis80PZqwqN2d98A5df7u6Y3njDTdFjylZSEvzf/8G4hzI5lB1Gg8hkQkS5pP1iFuxsTWRYJo/2nUaPJnlXzp66PoGvNnfgxbPfAWDymp4s2NmK8Wd+wMjZFzHvt+MY3HEhp7dcz4tL+/DS2e+Ua+eH8hLsgcfltbR6aRTaXVxEpqjqpSKyEj/9372JV00V0Gbg5UE/RxksIeSXqhunc999rj1k6lRo1y5IJ6vm6taFoUPhgxd28eTpU3l5eW+m/9KVCUv70r/Nak5vuZ6rPh7KzyMeCLg978nTD6+3dPFH1/PcWe/y2Px+rL7UTaxbzMQiFcqRfsEbAbBlPelzC19VqLoE7KLGMd3m/Ty3iH2MKTdJSa6g/OgjNzfca69B7drlnavq4fRW6zm91Xr6vXcrLaL38c2v7fl8c2fCQrJ46If+3Nx9Ll9t6cB/5p3HbwfqERmaSd+W67m8w2K2H4ihee28s3l8vCGebo1/I+VQBJsSGzLlO9f54sor3YS1pnopNDCp6k7v568520SkIbBHK1v9n6lyVq921XUbN7oG+dtus153R8v6OjG5d9mpGsKS3U2p/dxqak9rTPLLrXlk/gAeWdgfVCDb/VLSs8IZ+vVV/KPryaTtbE7d0euZ0MaloZnCvn92pN5jP/PStvNI/aMpk3ADow8dssBUHRXa+UFEThKROSIyVUS6iYjPmhoS0JoaxgTDu++6pc+TkmDWLLj9dgtKR8PgwXDyyZC5NZJdg04g9ZNYag7YRdbOSPZc15X0L2Kp9/gaGkxcBuHZuUEp16FQDjzWlohT9hLe5vCSbqkfNSGy/y4kMpuwY1PR9BDi490YNJ/JxU01UlRV3vPAvUBd3Joa56jqfBFpj1tno+CEUsYEUUaGW2V2/Hg3RmnKFDfnmzk63nF9F2g6d36e7TH/9jMG52BowW0A2ULKmy1I+zyWsDaphLVJJfwY91MPhiAR2cTc/wsr+zQq49ybyqSowBSmql8CiMhYVfXW1NC15bWom6m+du6ESy+F775zS4s/8YRbR8lUTCGxB8n+I7Lg9phD1Lp8BxmbosjcHEXqsqZwyKu4ESW0eTphbVK5/3S36GF8PBx3HBSyfJOpoor6dftORpJ/KXVrYzJHzXffwSWXwP79btCnz6KmpoKqfd1W9j9xbN47p4gsat+yhaizdudu0kzI2hFJ5uYoMjbVInNzFJmbonjoocPTIdWoAR06uCCVE6w6d4YWLawKt6oqKjB1FZH9eGtqeM/xXhf8KmRMGVOFZ5+FkSOhdWs3d1t8fHnnygQiJ/gkv9qS7F0RhMQepPZ1W/MEJQAJg7CW6YS1TCey797c7Zt6nsLatbByJaxa5X7OmeMWdcxRp44LUL7BqnNnaFjxpm00JVRUr7xCKomNCb6UFDeG5Z13YOBAt0RDaRe2M+Uj6qzdBQJRoGrWhG7d3MNXYqILVDnBatUqeP99N9tHjiZNCgarTp0gWBMgDBsGM2fCvtrjaPjGMgD2v9iKgz/UQ8KU0Gbp1L1nAyHRWQWOzT4QStITx5G52c0uXHfUBmp0TubAS604+FMMYcelEHOfW7ss7cuGjBnvOvtUdVZzayqcX35xXcFXr4aHH4Z77qn6C/qZwMTEQO/e7pFD1bVB+garVavcjPJpXiOEiJs13TdYxcfD8cdDgEsiFeraa+GWW6DXoMPbInokEn3dr0gYHHipFSlvxxF9468Fjt3/XBsiTtxHvbHr0AxB00PITg4lY30tGk5cTtLjx5KxMYqwuHTSPovl7/MLJFElWWAyFcr06W4xu/Bwt4ZSv37lnSNT0Ym43pnNmuX9e8nKgs2b8warlSvd3Y03fyrh4W7GkPxVgq1aBf5lqE8fyL+2YETPwwOIwzseIP3bBgWOy04OJWN5Her+y90RSbgi4Vlkp4agWYIqaHoIEqakvNuMqIt+Jzw8pgRXpvKywGQqhKwsGD0aHn3ULX/+wQeucDCmtEJDXY++446DCy88vP3gQVi7Nm+w+uGHw93hwc0g0qlT3mAVH1+6SYHTPo0l8oyCVZpZOyMIickgadxxZG6IIrxdCtG3biYkKpuIXvvYM6IrNbonIbUzyfi5NrWHbCvFVaicLDCZcrd7txu8+fXXrl3p2WfdUuDGBENEBHTt6h6+9u931ce+VYLTp4PPBOE0alQwWHXqVPi5kt9sDqFKpL+2tiwh45faRN+2mRodk9n/bGtSJjcnevhv1L5iB7Wv2AFA0uPHUnvYb6TOjOXS56FLF/CZRLxKspp7U64WLoTu3d1ifq+95hqxyzIoDRs2jNjY2Dzr1AC8//77dOrUiZCQkEKXyf7tt984/fTT6dixI506deKZZ57JfW/UqFF06dKFa665JnfbW2+9xfjx4wPKV+vWrYmPjychIYEePXrkbh8zZgzNmzcnISGBhIQEPv3ULQFx6NAhhg4dSnx8PF27dmXOnDkBXgETqDp13MwWOV+OZs2CXbvg99/dl6ann3YdcVJTXbC6/nq3f506rs0r6/caHHilJWlfNSRjYxSpH8dy8If6xIz+JU+39tSvGrLr0u7suaELKGRud3/wkX33kLk+72SPGetrgUJoizTS5zRgyhQ3DVcR6wpWCXbHZMqFqgtEt9wCTZu6dX9OOKHsz3Pttddyyy235AkgAJ07d2bq1KnccMMNhR4bFhbGU089Rffu3Tlw4AAnnHACZ511Fs2bN2fJkiWsWLGCESNGsHLlSo477jgmTpzI558HPiHK7Nmzaeinb/Mdd9zByJEj82x79dVXAVi5ciW7du3inHPOYeHChYRYr5CgEoHGjd3Ddw2/7Gz49dfDd1bz58P2z0NIebcZZOX8TpTQFmkkjTuOMG92i8wdEST/t+Xh8V3ZsP+xYwHI2laT0Napec6f/HoL6ozcCJmH5x0MCXHBsSqzwGSOurQ0F5Bef901Vk+eDA0Ktg2XiT59+rAlf8s05K4AWpSmTZvStGlTAKKjo+nQoQPbt2+nRYsWZGRkoKqkpqYSHh7Ok08+ya233kr4kXbxKsSaNWs444wzAIiNjSUmJoZFixZx4oknBuV8pmghIa6XX5s2rm1q4UIgG0LqZVDzvD9Im9YEPRhC9t4apM+LhNlFDK7KCGX/uOOIOGkfde/ZkLs5fV59wtqlENowA4Cw41KIj4+hS5eC1ZBVjX3dMkfVli2u2uP11109+aefBi8olaUtW7awdOlSevXqRXR0NAMGDKBbt240bdqUunXr8tNPP3FBCdYDFxH69evHCSecwCu+g3BwC8Z16dKFYcOGsW/fPgC6du3KjBkzyMzMZPPmzSxevJjffvutLD+iKaV33nHd1ZvMmk/sh4uJvnYbsdMW0fizBTT+dAFNvplP7Gfzqf/SCgqdNCdLqPfwujxjnSJP20v00MO/4zo3/8rKlW72k6rOApM5ar74wlXXbdwIM2bAgw+6nlMVXXJyMhdffDHjx4+nTp06ANx9990sW7aMp556itGjRzN27Fhee+01Lr30Uh566KFi0/zuu+9YsmQJn332GS+88AJz584F4KabbmLjxo0sW7aMpk2b8s9//hNwbWVxcXH06NGD22+/nVNOOYXQynDxDAAhUdnU6JBMSOOD/t+P9b+9urLAZIJOs+Ghh+Ccc6B5c1i0CM47r7xzFZiMjAwuvvhirrzySi7ys1770qVLUVXatWvH+++/z5QpU9i4cSO/FNM63bx5c8BVy1144YUsWLAAgMaNGxMaGkpISAjXXXdd7vawsDCefvppli1bxvTp00lMTOT4448v409rgq32dVshIt8MEBFZbrvJZYHJBFX2gVAS723P6NFu8tX58924kspAVRk+fDgdOnTgzjvv9LvP6NGjefDBB8nIyCDLG7UZEhJCamoq27dv50zfFnNPSkoKBw4cyH3+5Zdf5vYa3Lnz8CL0H330Ue721NRUUlJSAPjqq68ICwujY8eOZfdhzVERddZu6ty1kZDG6SBKSON06ty1sdRTN1VV1vnBBE3GxigSR7cj6/cInn8ebr756M8GPXjwYObMmcPu3buJi4vjgQceYPjw4Xz00Ufceuut/Pnnn/ztb38jISGBL774gh07djBixAg+/fRTvv/+e958883cbt0AjzzyCAMGDABg2rRp9OjRg2beolAJCQnEx8fTpUsXunbtyqJFiwjzs17DH3/8wYXeiM/MzEyuuOIK+vd3a2/mVBGKCK1bt+bll18GYNeuXZx99tmEhITQvHlz3nzzzWBfOhMkRzKHYHUhlW2V9Fq1amnON8eSajr3hzLOTUE7+5wS9HOUtWBcl7QvG5L05LGERGcS88B69txc+aYFTx/5jyM6/sWly2kRHc25xx1T6D6RTz57ROcoD/Z/5F9Fvy4ikqqqQZrKtmzZHZPJlTKlKWmfNAaBsDYp1L1nAxJx+ItL1u8RJD12LNmJ4UidTGLu+4XQ2ENkbo0k8cHjIVOIvn0TB2c3JPWjpkjtTOo/t4qwZtWzYfemblW8T68xQWKByQCQ9WcNUj9sSsP/LUMiskm8/3jSZjUk6pw/c/fZP6EVNc/+k5r9/+TgkjoceKUlMf/eQOqMJtS5dTPUyGbfyI7ogXDCuyVS869/VtugZIwpPev8YHJplqAHQ9BM0IMhhDY8lOf9rF+jqNHdzZpco9t+Dn5fHwAJUw6tjmbfXR3Q1FDqjPoFCYWaA/4scA5jjCmOBSYDQGijQ9S6fAd/XnoCuy7qidTKyjN1P0DYsSmkz3WjYQ/Oq4+mhpGVGAZh2SS/1ApNDyVmzDoyN9ei1lXbEPvrMsaUghUdBnDdug9+V59G7y4mduoiND2EtC/zTqMSffMWDi2rw+7hXTi0rA4hDQ6S9PixpLzZgoi+e4mdtoiwY9LI/rMGYa3SSHzoOBLHHE/mbzZVuDEmcBaYDACHFsUQ2jSdkJhMJEyJPG0vh1bVybNPaMMMIvruIftAGKkfNiV7Tw0OfV+f6Bu3EPPAOkJqZZH8Wktqj9hK6odNiTp3F9E3/kryGy3K6VMZYyoj6/xgAAhpfJCMNdFoeghEZHNwSV3C2yXn2Sd5eizJz7eBQz5T4YRnIw0PIYK7i2p4iLC4dPRgCIhCiLo0jTEmQBaYDAA1OiYT0XcPu4d3deu/ND4I7ZU9t3SGEIVsIWNVNGi+EbIZISS/2pKaf91N8v/iiBmzHoCoc/8g8aG2kCXUuXMTUAlmajXGVAgWmKqZpCS3jozvI3HR8WT9HkHWHxFk76sBQNb2mhxaEgOhSkijgy5QFTIWO3tXBCJQ///W5G4La51Gw9dWHIVPZIypaqplYMo+EErSE8eRubkmAHVHbaBG58PVVgeX1iHxvvaENnVjcCJP20Pta7eRnRjGvn+3R5PDqD18K5Gn7QVg373tqXPnxtx1U8qLqlum3DfobNmSLwgl5j0mIgIyY2sR2vggEcfuI7SJC0KhTdIJbXKQkAaHEO+vZNel3cn+o2BHhqo6M3KbgZcH/Rw7i9/FmGqnWgam/c+1IeLEfdQbuw7NEL9tIDW67KfeuLV5tqV93ZCogb8T2Wcve0d1IPK0vaR/X4+wtilHJShlZ7t1XwoLOr/+WnBly+hoaNXKPU49FVq3Pvy6VSuIjYXm3y0N6Py1r9vK/ieOPbz6JtjMyMaYMlftAlN2cigZy+tQ919upUgJVyQ8q5ijHAlTND0UzRAkRNFMSP2gKfUeXVv8wQHIyIBt2woPOlu3un18NWjgAkz79tC/f96g07o1xMSU3cSpORNPJr/akuxdEYTEHqT2dVttQkpjTJmqdoEpa2cEITEZJI07jswNUYS3SyH61s2E1MzOs9+h1dHsHtaVkAaHiL55C+Ft0oj8626SHjye1JmNib7hV1KnNSGy359IZHYhZ8srLc0Fl/xBJ+f1jh3urshX06YuwPTsCYMG5Q06LVtC7dplcVUCZzMjG2OCrdoFJrKEjF9qE33bZmp0TGb/s61Jmdyc6OGHlzAOPz6F2jduIWVSCzI3xrBncVfq/GsDUWftpt5jPwOunSrl7ebEPLSWpMePJTs5jKjzfmdFTOFtPLt25c1KaCi0aOECzRln5A06rVq59yIijtqVMcaYCqHaBaaQRocIaXSQGh1dZ4fIvntImRyXZ5/0H+px4JljDrelZAn7Hz8WTQ2hRrsUsv6IIOXDpoTUyWTvLfFkHwgjOyWUg982wHc+6YiIw8Gma9eC7TvNmoGf5XqMMaZaq3bFYmiDDEIbuaUawlqmc3BJDKGt8/YYOPBSy7wN/ACHQjnwf3mXXpWoTCQyi5D6GUT02suhRfWYPD4y964nNvboL4xnjDGVXbULTAB1bttE4kPHQ4YQ2iyduvdsIHV6YwCizv8D3V1Y/ZkS89A6Uqc1pvZ1vxLeLpXsxHAS72tHxsq6RN+0hUsvbX/0PogxxlRB1TIwhbdNpeEreQd/Rp3/R+7zkMYH/Y/XaXyQyNP25o5fAgitl0GDCauCl1ljjKlmbBIzP2pftxUi8nUht/E6xhhzVFTLO6bi2HgdY4wpPxaYCmHjdYwxpnxYVZ4xxpgKxQKTMcaYCsUCkzHGmArFApMxxpgKxQKTMcaYCsUCkzHGmArFApMxxpgKxQKTMcaYCsUCkzHGmArFApMxxpgKxQKTMcaYCsUCkzHGmArFApMxxpgKxQKTMcaYCsUCkzHGmArFApMxxpgKJaiBSUT6i8g6EdkgIvf4eT9CRN7z3v9JRFoHMz/GGGP8E6G/COtE2CCCn/KaCBHe897/SYTWwcpL0AKTiIQCLwDnAB2BwSLSMd9uw4F9qnoc8DTwWLDyY4wxxj8R/JTXFFJeE/TyOph3TCcCG1R1k6oeAt4Fzs+3z/nAJO/5B8CZIiJBzJMxxpiCvPKaTaqUoLwmKOV1WDAS9TQHfvN5vQ3oVdg+qpopIklAA2C3704icj1wvfdSRSQtKDn2LwzIDHTnahRV7boUVKJrAnZdCmPXxb8juy4RNUVY5LPhFVVe8Z6XsLwmUwS/5XVZCGZgKjOq+grkXsCjSkQWqWqP8jh3RWbXpSC7Jv7ZdfHPrkvhglmVtx1o4fM6ztvmdx8RCQPqAnuCmCdjjDEFlbC8JqjldTAD00KgrYi0EZEawOXAjHz7zACGeM8HAbNUVYOYJ2OMMQV55TVtRChBeU1QyuugVeV5bUa3AF8AocDrqrpaRMYCi1R1BvBf4E0R2QDsxV2MiqZcqhArAbsuBdk18c+ui38V5rp4bUb5ymtWi+CV1/iU1wS9vBa7QTHGGFOR2MwPxhhjKhQLTMYYYyqUahuYRGSgv2mSAjjuh2DkxxhjjFNtA5OqzlDVcaU47pRg5KesiUhrEUkTkWV+3gsoKIvIEyKyWkSeKOT9niKSKSKDfLYNEZFfvMeQQo6rLyJfeft8JSL1vO3nep1jjpi/zy8ioSKyVERm+mxr483TuMGbt7FGIen9y9tnnYicXcg+D4vIbyKSXMj7F4uIikgPn22BpOs3jyJyi4gMK8l1EJHXRWSXiKwqZP9/enls6L0WEXnWO/cKEeleyHEniMhKb79n/c3gIiI3evssE5HvfKcoE5EuIvKj9/e2UkQiS5Cu3zyKSCMR+byo61MY3+smIpEiskBElnv5eyDfuR8WkfUi8rOI/CNI1+2aAPL8jneuO0TkSRE5ozSfvUJQ1Sr3AFoDa4E3gPXA28Bfge+BX3DTb1wLPO/tfwmwClgOzPW2dQIWAMuAFUBbb3uy9/MvwBzc1BxrvXPkdCYZ4G1bDDwLzCyna7DqCNNIAkILeS8UmAV8CgzyttUHNnk/63nP6/k59nHgHu/5PcBj3nMBlgJRwfj8wJ3AZN/fBzAFuNx7/hJwk5+0Onp/GxFAG2Cjv+sCnAQ0zfkbyfdeNDAXmA/0KGG6fvMIRAFLS3IdgD5Ad39/G7gxKl8AvwINff6WP/N+NycBPxVyngXe++Ltf46ffer4PB8IfO49D/P+x7p6rxvkXIcA0y00j8BE4NQj+fvx0q3tPQ8HfgJO8l4PBf4HhHivY8v6ugWY3ya4KeByXrcCvjzS/6PyelTlO6bjgKeA9t7jCqA3MBK4N9++/wHOVtWuuH8YgBuBZ1Q1AeiBm6Ijv27A7bgC5hjgVO+b3su4P7ATgEZl95HKhohcKyLPe8/f8L6p/SAim8S7+xGRGUBtYLGIXOYnmVuBD4FdPtvOBr5S1b2qug/4Cujv51jfObcmARcAqPuPmgOce4QfsQARiQP+Brzms02AM3BfLvLkxU9+31XVg6q6GdiA+3KTh6rOV9WdhWThQdykl+klSbeoPKpqKrBFRArkpTCqOhfX1defp4G7Ic/YlPOB/6kzH4gRkab58tgUF3Tme7/D/+HnOqrqfp+XtXzO0w9YoarLvf32qGpWoOkWk8dpwJWFfN6AeOnm3AWHe4+cvN8EjFXVbG/fnP+HMrtuIjJGREZ6z+eIyGPeHdx6ETnN2+1LoLl3h3eaqv4KNBCRJkfy2ctLVQ5Mm1V1pfcHsxr4xvvlr4QC07V/D7whItfh7gQAfgTuFZFRQCtV9Tc/3wJV3eadY5mXbntgk1fQALxThp8pWJrigva5wDgAVR0IpKlqgqq+57uziDQHLgRezJeOv/m2mvs5X2OfAvx3oLHPe4uA0woecsTG4wrdbJ9tDYBEVc2Zr6yw/Ab6ufzyqnFaqOonpUi3uDyWyfUSkfOB7TnBoYR5bE7eL26FXh8R+buIbMTdNf/D23w8bg7ML0RkiYjcXcJ0i8pjWV2fUHHVobtwX75+8t46FrhMRBaJyGci0jaAPPnmO6Drlk+Yqp6I+1J8v7dtILDR+3+d521bApwaQHoVTlUOTAd9nmf7vM4m38BiVb0R+DeuKmOxiDRQ1cm4X3Ya8Gkh9bW+58jKn24lMk1Vs1V1DXmDRGHGA6NyviUeCe/Lgu839F1AsyNN15eInAvsUtXFZZlugOcOAf4P+GeQTnHE10tEonC1CP8pkxwVQVVfUNVjgVG4/zlw/ze9cXc2vYELReTMMjplmfw9qWqWV3sSB5woIp29tyKAdHVz3r0KvH6k5wrAVO/nYgp+yfZV5v9LR0tVDkwBE5FjVfUnVf0P8CfQQkSOwd35PAtMB7oEmNw64Bg5vOihv2qwo8r7lrrMe/j7Q/UNsIFMYNwDeFdEtuCmJpkgIhcQ2HxbAH/kVGt4P32rAyNxXwbK0qnAQC+/7wJniMhbuHm+YsTN01hUfgP9XP5EA52BOd75TwJmiOsAEUi6xeWxLK7Xsbg2ruVeHuOAJV41UKBzqMUVs09+73K42mobrm13t1c9+SmuHSzQdIvKY5n+PalqIjCbw1XU2zgcKD7icDkRrOsGh/9fi/syHIz/paPCApPzhNczZhXwA65B+lJglXf73hlX/1ssr8rvZuBzEVkMHMB1Iig33rfUBO+xozRpiMiFIvKol14bVW2tqq1xbR83q+o0XMN5PxGpJ66nXT9vW36+c24NwQX+HMfjOqKUGVX9l6rGefm9HDcn41Xe3dpsXHD1lxff/F4ubsXlNkBbXKM1IvKNV7VZ2LmTVLWhz/WaDwxU1UVFpetzfHF5POLr5VV5x/rkcRvQXVV/9/J4jdfL7CQgKX87mvd6v4ic5LWJXZOTR3E9B2/xnrf1OexvuI5I4P5G4kUkygvAfYE1RaWbT1F5POLrI653X4z3vCZwFq5zE7g2rNO9531xna2KyxMQ+HU7AmX+v3TUaJB7V1THB4d78AgwAbijHPLQmkJ65ZG3R+IbeL3qvNfJhTwfCfzLT1r5jx+Ga8TfAAz12f4ah3ujNQC+wRVMXwP1ffabCcQH6/PjelP69so7BhcMNgDvAxHe9oG4Ru2c/e7D9Zpbh9dzCvfF7legpvf6cVyhnu39HOPn/HNyrkNh6XrbPwWaFZVH770lQINArwOuzXMnkOHlcbifY7ZwuFee4FY23Yhrn/XN+zKf5z1wheBG4HkO91B9HhjsPX8G1967DBdsO/kcf5X33irg8QDSvRG4MYA8jgRuPZK/H9xd0FJcz8FVwH989osBPvHO+yOHexaW5XUbA4zM//cDNAS2FPJ7Dgd+xrVHlUs5eCQPmysvCETkDtw32xq4P+jr1FVRHM08tMYVwJ2L2zfA9N7CBdg/yyK9Qs7RGJisqkfcvlDWn7+Qc3QGhqnqncE6RzHn7wbcqapXF7FPa4J8HYoibszYRepWsS6P888FzlfXS7Qkx7WmEl83EbkQd9c7umxzdnRYYKqiRKQFrlpyj7pG2wpPRHoCGaq6rAzSqnSfv6RE5CzgF1XdUsQ+Vf46FEZEGuHGME0rxbGV+rqJyCW43oOJ5Z2X0rDAZIwxpkKxzg/GGGMqFAtMxhhjKhQLTMYYYyoUC0zGGGMqlP8HQRFZkspu8toAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report(data,\n",
    "       data_train,\n",
    "       data_test,\n",
    "       data_oot=None,\n",
    "       model=bst,\n",
    "       y='flagy',\n",
    "       filename='1',\n",
    "       points0=45,\n",
    "       pdo=-23,\n",
    "       odds0=0.133,\n",
    "       grey=2,\n",
    "       score_range=(0, 100),\n",
    "       tick=10,\n",
    "       percent=5,\n",
    "       top_n = 10,\n",
    "       user_data = 'user_date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Mon Jul 26 19:41:06 2021\n",
    "\n",
    "@author: chang.lu\n",
    "\"\"\"\n",
    "\n",
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pylab as plt\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import scorecardpy as sc\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from collections import Counter\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "import os\n",
    "import warnings\n",
    "import datetime as dtt\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "def report(\n",
    "    data_total, \n",
    "    data_train, \n",
    "    data_test,\n",
    "    data_oot = None,\n",
    "    model = None,\n",
    "    y = 'flagy',\n",
    "    filename='',\n",
    "    points0 = 55,\n",
    "    pdo = -10,\n",
    "    odds0 = 0.1,\n",
    "    grey = 2,\n",
    "    score_range = (0,100),\n",
    "    tick = 10,\n",
    "    percent = 5,\n",
    "    top_n = 10,\n",
    "    **kwargs):\n",
    "    \"\"\"\n",
    "    :param data_total: dataframe 所有训练样本，不含验证集，包含入模变量、标签及申请日期，申请日期最好为日期格式，有灰客户样本\n",
    "    :param data_train: dataframe 训练集，只包含入模变量及标签，不含灰客户\n",
    "    :param data_test: dataframe 测试集，只包含入模变量及标签，不含灰客户\n",
    "    :param data_oot: dataframe 验证集，默认为None，不含灰客户\n",
    "    :param model: 最终模型,默认为bst\n",
    "    :param y: str 标签名，默认为'flagy'\n",
    "    :param filename: str 报告名，默认为'',输出名称自动加'report_生成日期'后缀，如'lgb模型report_2021_07_23'\n",
    "    :param points0: int 基准分，默认55\n",
    "    :param pdo: int pd0，默认-10\n",
    "    :param odds0: float 坏账率比，默认0.1\n",
    "    :param grey: int, float or str 灰客户的取值标识，默认取2\n",
    "    :param score_range: tuple 评分的上下限，如 (0, 100)\n",
    "    :param tick: int or float 评分分布的分数间隔，默认为10\n",
    "    :param percent: int or float 评分等频分布的分位数间隔，默认为5，即5%分位数\n",
    "    :param top_n: int 输出重要性为前n的变量分箱图，默认为10，即输出变量重要度为前10的变量分箱图\n",
    "    :param kwargs: 其他变量,如user_date='user_date'：申请日期名称，出现在data_total中，主要用于报告第2部分样本分析\n",
    "    \"\"\"\n",
    "    # 文件名\n",
    "    filename = filename + 'report_' + dtt.datetime.now().strftime('%Y_%m_%d')\n",
    "    # 取出模型中的变量,确认函数中传入的样本包含入模变量和标签\n",
    "    var_final = model.booster_.feature_name()\n",
    "    data_train = data_train[var_final + [y]].copy() # 训练集只取入模变量和y\n",
    "    data_test = data_test[var_final + [y]].copy() # 测试集只取入模变量和y\n",
    "    try:\n",
    "        data_oot = data_oot[var_final + [y]].copy() # 验证集只取入模变量和y\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    # 进行excel报告内容整理\n",
    "    table = pd.ExcelWriter(filename + '.xlsx',engine = 'xlsxwriter')\n",
    "    # ------------------------------------------------------------------------------------------------------------------\n",
    "    # 目录页\n",
    "    sheet = pd.DataFrame(\n",
    "        columns=[\"编号\", \"中文简称\", \"英文简称\", \"内容\"],\n",
    "        data=[\n",
    "            [\"1\", \"模型使用说明\", \"Model_Explain\", \"模型使用说明\"],\n",
    "            [\"2\", \"原始数据统计\", \"Original_Stat\", \"原始数据情况、建模数据选取、匹配百融数据说明\"],\n",
    "            [\"3\", \"衍生特征构造\", \"Var_derivation\", \"衍生特征构造\"],\n",
    "            [\"4\", \"数据预处理-格式转换\", \"Data_Pre_Format\", \"数据预处理-格式转换\"],\n",
    "            [\"5\", \"模型参数\", \"Model_Params\", \"模型参数及评分参数设定\"],\n",
    "            [\"6\", \"模型区分度评估\", \"Model_Disc\", \"模型区分度评估\"],\n",
    "            [\"7\", \"模型稳定性评估\", \"Model_Stab\", \"模型稳定性评估\"],\n",
    "            [\"8\", \"单变量稳定性\", \"Var_Stab\", \"单变量稳定性评估\"],\n",
    "            [\"9\", \"变量重要性\", \"Var_Importance\", \"单变量重要性排序\"],           \n",
    "            [\"10\", \"变量趋势\", \"Var_trend\", \"重要变量趋势\"],\n",
    "            [\"11\", \"样本风险评分分布\", \"Model_Score\", \"模型评分及风险表现\"],\n",
    "            [\"12\", \"评分决策表\", \"Decision_table\", \"不同评分分段的通过率、违约率提升\"],\n",
    "        ],\n",
    "    )\n",
    "    sheet.to_excel(table, sheet_name=\"目录\", startrow=0, startcol=0, index=False)\n",
    "    # -------------------------------------------------------------------------------------------------------------------\n",
    "    # 1.模型使用说明页\n",
    "    head = pd.DataFrame(columns=[\"返回目录\"])\n",
    "    sheet1 = pd.DataFrame(\n",
    "        index=[\"版本名称\", \"模型类型\", \"客群种类\", \"该版本更新时间\", \"开发人员\", \"建模样本数据量\", \"模型变量数量\", \"核心算法\"],\n",
    "        columns=[\"内容\"],\n",
    "    )\n",
    "    head.to_excel(table, sheet_name=\"1.模型使用说明\", startrow=0, index=False)\n",
    "    sheet1.to_excel(table, sheet_name=\"1.模型使用说明\", startrow=1)    \n",
    "    # -------------------------------------------------------------------------------------------------------------------\n",
    "    # 2.原始数据统计页\n",
    "    head2_1 = pd.DataFrame(columns=[\"一、数据来源\"])\n",
    "    sheet2_1 = pd.DataFrame(\n",
    "        index=[\n",
    "            \"机构\",\n",
    "            \"产品类型\",\n",
    "            \"业务开展时间\",\n",
    "            \"引流渠道\",\n",
    "            \"额度区间\",\n",
    "            \"期数范围\",\n",
    "            \"存量客户数量\",\n",
    "            \"日进件量\",\n",
    "            \"平均通过率\",\n",
    "            \"审批流程\",\n",
    "            \"审批使用数据\",\n",
    "        ],\n",
    "        columns=[\"内容\"],\n",
    "    )\n",
    "    head2_2 = pd.DataFrame(columns=[\"二、数据概要\"])\n",
    "    sheet2_2 = pd.DataFrame(\n",
    "        index=[\n",
    "            \"客群描述\",\n",
    "            \"观察期\",\n",
    "            \"表现期\",\n",
    "            \"原始样本时间\",\n",
    "            \"原始样本量\",\n",
    "            \"建模样本时间\",\n",
    "            \"建模样本量\",\n",
    "            \"验证样本时间\",\n",
    "            \"验证样本量\",\n",
    "        ],\n",
    "        columns=[\"内容\"],\n",
    "    )\n",
    "\n",
    "    head2_3 = pd.DataFrame(columns=[\"三、好坏客户定义\"])\n",
    "    sheet2_3 = pd.DataFrame(columns=[\"客户类型\", \"定义方式\", \"样本量\", \"好坏客户定义描述\"])\n",
    "    sheet2_3[\"客户类型\"] = [\"坏客户\", \"灰客户\", \"好客户\"]\n",
    "\n",
    "    head2_4 = pd.DataFrame(columns=[\"四、建模数据统计情况\"])\n",
    "    sheet2_4 = pd.DataFrame(columns=[\"年\", \"月\", \"数量\", \"比例\", \"坏数量\", \"坏账率\", \"平均坏账率\"])\n",
    "    if \"user_date\" in kwargs.values():\n",
    "        data_total['user_date'] = pd.to_datetime(data_total['user_date'], errors=\"coerce\")\n",
    "        temp = data_total.copy()\n",
    "        temp[\"年\"] = temp['user_date'].apply(lambda x: str(x.year) + \"年\")\n",
    "        temp[\"月\"] = temp['user_date'].apply(lambda x: str(x.month) + \"月\")\n",
    "        temp = (\n",
    "            temp[temp[y] != grey]\n",
    "            .groupby([\"年\", \"月\"])[y]\n",
    "            .agg([len, sum])\n",
    "            .rename(columns={\"len\": \"数量\", \"sum\": \"坏数量\"})\n",
    "            .reset_index()\n",
    "        )\n",
    "        temp[\"比例\"] = temp[\"数量\"] / temp[\"数量\"].sum()\n",
    "        temp[\"坏账率\"] = temp[\"坏数量\"] / temp[\"数量\"]\n",
    "        temp[\"平均坏账率\"] = temp[\"坏数量\"].sum() / temp[\"数量\"].sum()\n",
    "        temp = temp[[\"年\",\"月\",\"数量\", \"比例\", \"坏数量\", \"坏账率\", \"平均坏账率\"]]\n",
    "        sheet2_4 = temp.copy()\n",
    "\n",
    "    head2_5 = pd.DataFrame(columns=[\"五、建模数据选取\"])\n",
    "    sheet2_5 = pd.DataFrame(columns=[\"类型\", \"年\", \"月\", \"数量\", \"比例\", \"坏数量\", \"坏账率\", \"平均坏账率\"])\n",
    "    sheet2_5[\"类型\"] = [\"训练\", \"测试\", \"验证\"]\n",
    "    if \"user_date\" in kwargs.values():\n",
    "        data_train[\"user_date\"] = data_total['user_date']\n",
    "        data_test[\"user_date\"] = data_total['user_date']\n",
    "        data_train[\"类型\"] = \"训练\"\n",
    "        data_test[\"类型\"] = \"测试\"\n",
    "        try:\n",
    "            data_oot[\"类型\"] = \"验证\"\n",
    "            data_oot[\"user_date\"] = data_oot['user_date']\n",
    "        except:\n",
    "            pass\n",
    "        data_merge = pd.concat([data_train, data_test, data_oot], axis=0, sort=False)\n",
    "        data_merge[\"年\"] = data_merge['user_date'].apply(lambda x: str(x.year))\n",
    "        data_merge[\"月\"] = data_merge['user_date'].apply(lambda x: str(x.month) + \"月\")\n",
    "        data_merge = (\n",
    "            data_merge.groupby([\"类型\", \"年\", \"月\"])[y]\n",
    "            .agg([len, sum])\n",
    "            .rename(columns={\"len\": \"数量\", \"sum\": \"坏数量\"})\n",
    "            .reset_index()\n",
    "        )\n",
    "        data_merge[\"比例\"] = data_merge[\"数量\"] / data_merge[\"数量\"].sum()\n",
    "        data_merge[\"坏账率\"] = data_merge[\"坏数量\"] / data_merge[\"数量\"]\n",
    "        data_merge[\"平均坏账率\"] = data_merge[\"坏数量\"].sum() / data_merge[\"数量\"].sum()\n",
    "        data_merge = data_merge[[\"类型\",\"年\",\"月\",\"数量\", \"比例\", \"坏数量\", \"坏账率\", \"平均坏账率\"]]\n",
    "        sheet2_5 = data_merge.copy()\n",
    "        del data_train['user_date'],data_test['user_date'],data_train['类型'],data_test['类型']\n",
    "        try:\n",
    "            del data_oot['user_date'],data_oot['类型']\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "    head2_6 = pd.DataFrame(columns=[\"六、数据集划分\"])\n",
    "    sheet2_6 = pd.DataFrame(columns=[\"数据量\", \"坏样本\", \"坏账率\"], index=[\"训练集\", \"测试集\", \"验证集\"])\n",
    "    try:\n",
    "        sheet2_6[\"数据量\"] = [data_train.shape[0], data_test.shape[0], data_oot.shape[0]]\n",
    "        sheet2_6[\"坏样本\"] = [data_train[y].sum(), data_test[y].sum(), data_oot[y].sum()]\n",
    "        sheet2_6[\"坏账率\"] = sheet2_6[\"坏样本\"] / sheet2_6[\"数据量\"]\n",
    "    except:\n",
    "        sheet2_6[\"数据量\"] = [data_train.shape[0], data_test.shape[0], 0]\n",
    "        sheet2_6[\"坏样本\"] = [data_train[y].sum(), data_test[y].sum(), 0]\n",
    "        sheet2_6[\"坏账率\"] = sheet2_6[\"坏样本\"] / sheet2_6[\"数据量\"]\n",
    "\n",
    "    head.to_excel(table, sheet_name=\"2.原始数据统计\", startrow=0, index=False)\n",
    "    head2_1.to_excel(table, sheet_name=\"2.原始数据统计\", startrow=1, index=False)\n",
    "    sheet2_1.to_excel(table, sheet_name=\"2.原始数据统计\", startrow=3, startcol=1)\n",
    "    head2_2.to_excel(table, sheet_name=\"2.原始数据统计\", startrow=16, index=False)\n",
    "    sheet2_2.to_excel(table, sheet_name=\"2.原始数据统计\", startrow=18, startcol=1)\n",
    "    head2_3.to_excel(table, sheet_name=\"2.原始数据统计\", startrow=29, index=False)\n",
    "    sheet2_3.to_excel(\n",
    "        table, sheet_name=\"2.原始数据统计\", startrow=31, startcol=1, index= False\n",
    "    )\n",
    "    head2_4.to_excel(table, sheet_name=\"2.原始数据统计\", startrow=36, index=False)\n",
    "    if sheet2_4.shape[0] == 0:\n",
    "        sheet2_4.to_excel(\n",
    "            table, sheet_name=\"2.原始数据统计\", startrow=38, startcol=1, index=False\n",
    "        )\n",
    "    else:\n",
    "        sheet2_4.to_excel(table, sheet_name=\"2.原始数据统计\", startrow=38, startcol=1,index=False)\n",
    "    row_number = sheet2_4.shape[0] + 38 + 17\n",
    "    head2_5.to_excel(table, sheet_name=\"2.原始数据统计\", startrow=row_number, index=False)\n",
    "    sheet2_5.to_excel(table, sheet_name=\"2.原始数据统计\", startrow=row_number + 2, startcol=1,index = False)\n",
    "    row_number1 = row_number + 2 + sheet2_5.shape[0] + 2\n",
    "    head2_6.to_excel(table, sheet_name=\"2.原始数据统计\", startrow=row_number1, index=False)\n",
    "    sheet2_6.to_excel(\n",
    "        table, sheet_name=\"2.原始数据统计\", startrow=row_number1 + 2, startcol=1\n",
    "    )\n",
    "    # ---------------------------------------------------------------------------------------------------\n",
    "    # 3.衍生变量构造\n",
    "    sheet3 = pd.DataFrame(columns=[\"序号\", \"模块\", \"变量\", \"中文名\", \"数据来源\", \"衍生逻辑\"])\n",
    "    head.to_excel(table, sheet_name=\"3.衍生变量构造\", index=False)\n",
    "    sheet3.to_excel(table, sheet_name=\"3.衍生变量构造\", startrow=2, index=False)\n",
    "    # ----------------------------------------------------------------------------------------------------\n",
    "    # 4.数据预处理\n",
    "    sheet4 = pd.DataFrame(columns=[\"序号\", \"变量\", \"数据源\", \"变量类型\", \"编码(转换)格式\", \"举例\"])\n",
    "    head.to_excel(table, sheet_name=\"4.数据预处理\", index=False)\n",
    "    sheet4.to_excel(table, sheet_name=\"4.数据预处理\", startrow=2, index=False)\n",
    "    # -----------------------------------------------------------------------------------------------------\n",
    "    # 5.模型参数\n",
    "    col = list(str(bst).split(','))\n",
    "    model_param = pd.DataFrame(col,columns = ['parms'])\n",
    "    model_param[['parms','参数值']] = model_param.parms.str.split('=',2,expand=True)\n",
    "    model_param['parms'] = model_param['parms'].apply(lambda x : x.replace('\\n',''))\n",
    "    model_param['parms'] = model_param['parms'].apply(lambda x :str(x).strip())\n",
    "    model_param.iloc[0,0] = model_param.iloc[0,0][15:]\n",
    "    model_param.iloc[13,1] = model_param.iloc[13,1][:-1]\n",
    "    mapping = pd.DataFrame({'parms':['bagging_fraction','feature_fraction','bagging_freq','importance_type','learning_rate',\n",
    "                                'max_bin','max_depth','min_data_in_leaf','n_estimators',\n",
    "                                'num_leaves','objective','random_state','reg_alpha','reg_lambda'],\n",
    "                            '模型参数':['每次抽取的样本比例','每次抽取的特征比例','装袋频率','重要性计算方式',\n",
    "                               '学习率','最大分箱数量','最大树深','每个叶子节点的最少样本量',\n",
    "                                '学习器数量','叶子结点数量','分类方式','随机参数','一阶惩罚项','二阶惩罚项']})\n",
    "    model_param = pd.merge(model_param,mapping,how = 'left',on = 'parms')\n",
    "    sheet5 = model_param[['parms','模型参数','参数值']].copy()\n",
    "    \n",
    "    head.to_excel(table, sheet_name=\"5.模型参数\", index=False)\n",
    "    sheet5.to_excel(table, sheet_name=\"5.模型参数\", index=False, startrow=2)\n",
    "    # -----------------------------------------------------------------------------------------------------\n",
    "    # 6.模型区分度评估\n",
    "    title = pd.DataFrame(columns=[\"模型区分度评估\"])\n",
    "    sheet6 = pd.DataFrame(columns=[\"评估指标\", \"训练集\", \"测试集\", \"验证集\"])\n",
    "    sheet6[\"评估指标\"] = [\"KS\", \"AUC\"]\n",
    "    # 计算KS、AUC\n",
    "    # 预测概率\n",
    "    train_class_pred = model.predict_proba(data_train.drop(columns = [y]))[:,1]\n",
    "    test_class_pred = model.predict_proba(data_test.drop(columns = [y]))[:,1]\n",
    "    try:\n",
    "        oot_class_pred = model.predict_proba(data_oot.drop(columns = [y]))[:,1]\n",
    "    except:                                            \n",
    "        pass\n",
    "    # 评估\n",
    "    train_perf = sc.perf_eva(data_train[y], train_class_pred, title=\"train\")\n",
    "    test_perf = sc.perf_eva(data_test[y], test_class_pred, title=\"test\")\n",
    "    train_perf[\"pic\"].savefig(\"train_KS_AUC.png\", bbox_inches=\"tight\")\n",
    "    test_perf[\"pic\"].savefig(\"test_KS_AUC.png\", bbox_inches=\"tight\")\n",
    "    sheet6[\"训练集\"] = [train_perf[\"KS\"], train_perf[\"AUC\"]]\n",
    "    sheet6[\"测试集\"] = [test_perf[\"KS\"], test_perf[\"AUC\"]]\n",
    "    try:\n",
    "        oot_perf = sc.perf_eva(data_oot[y], oot_class_pred, title=\"oot\")\n",
    "        oot_perf[\"pic\"].savefig(\"oot_KS_AUC.png\", bbox_inches=\"tight\")\n",
    "        sheet6[\"验证集\"] = [oot_perf[\"KS\"], oot_perf[\"AUC\"]]\n",
    "    except:\n",
    "        pass\n",
    "    title1 = pd.DataFrame(\n",
    "        columns=[\n",
    "            \"此次建模，训练样本KS={}，AUC={}，模型结果较理想，模型对好坏客户具有很好的区分度，且模型较稳定，达到建模预期目标\".format(\n",
    "                train_perf[\"KS\"], train_perf[\"AUC\"])])\n",
    "    title2 = pd.DataFrame(columns=[\"训练集\", \"KS={}\".format(train_perf[\"KS\"])])\n",
    "    title3 = pd.DataFrame(columns=[\"测试集\", \"KS={}\".format(test_perf[\"KS\"])])\n",
    "    title.to_excel(table, sheet_name=\"6.模型区分度评估\", index=False, startrow=1)                                     \n",
    "    title1.to_excel(table, sheet_name=\"6.模型区分度评估\", index=False, startrow=8)\n",
    "    title2.to_excel(table, sheet_name=\"6.模型区分度评估\", index=False, startrow=10, startcol=3)\n",
    "    title3.to_excel(table, sheet_name=\"6.模型区分度评估\", index=False, startrow=10, startcol=11)\n",
    "    try:\n",
    "        title4 = pd.DataFrame(columns=[\"验证集\", \"KS={}\".format(oot_perf[\"KS\"])])\n",
    "        title4.to_excel(\n",
    "            table, sheet_name=\"6.模型区分度评估\", index=False, startrow=10, startcol=19\n",
    "        )\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    head.to_excel(table, sheet_name=\"6.模型区分度评估\", index=False)\n",
    "    sheet6.to_excel(table, sheet_name=\"6.模型区分度评估\", index=False, startrow=2)\n",
    "    # 曲线图\n",
    "    sheet = table.book.sheetnames[\"6.模型区分度评估\"]\n",
    "    sheet.insert_image(\"A12\", \"train_KS_AUC.png\", {\"x_scale\": 0.9, \"y_scale\": 0.9})\n",
    "    sheet.insert_image(\"I12\", \"test_KS_AUC.png\", {\"x_scale\": 0.9, \"y_scale\": 0.9})\n",
    "    try:\n",
    "        sheet.insert_image(\"Q12\", \"oot_KS_AUC.png\", {\"x_scale\": 0.9, \"y_scale\": 0.9})\n",
    "    except:\n",
    "        pass\n",
    "    # -------------------------------------------------------------------------------------------------------------------------\n",
    "    # 7.模型稳定性评估\n",
    "    title1 = pd.DataFrame(columns=[\"1.训练&测试\"])\n",
    "    title2_1 = pd.DataFrame(columns=[\"等间距\", \"模型样本量分布评估\"])\n",
    "    title2_2 = pd.DataFrame(columns=[\"等频\", \"模型样本量分布评估\"])\n",
    "    head.to_excel(table, sheet_name=\"7.模型稳定性评估\", index=False)\n",
    "    title1.to_excel(table, sheet_name=\"7.模型稳定性评估\", index=False, startrow=1)\n",
    "    title2_1.to_excel(table, sheet_name=\"7.模型稳定性评估\", index=False, startrow=2)\n",
    "    title2_2.to_excel(\n",
    "        table, sheet_name=\"7.模型稳定性评估\", index=False, startrow=2, startcol=11\n",
    "    )\n",
    "    B = pdo / np.log(2)\n",
    "    A = points0 + B * np.log(odds0)\n",
    "    score_train = np.around(A - B * np.log(train_class_pred/(1 - train_class_pred)))\n",
    "    score_train = pd.DataFrame(score_train,index = data_train.index)\n",
    "    score_train = pd.concat([data_train['flagy'],score_train],axis = 1).rename(columns = {0:'score'})\n",
    "    score_test = np.around(A - B * np.log(test_class_pred/(1 - test_class_pred)))\n",
    "    score_test = pd.DataFrame(score_test,index = data_test.index)\n",
    "    score_test = pd.concat([data_test['flagy'],score_test],axis = 1).rename(columns = {0:'score'})\n",
    "    try:\n",
    "        score_oot = np.around(A - B * np.log(oot_class_pred/(1 - oot_class_pred)))\n",
    "        score_oot = pd.DataFrame(score_oot,index = data_oot.index)\n",
    "        score_oot = pd.concat([data_oot['flagy'],score_oot],axis = 1).rename(columns = {0:'score'})\n",
    "    except:\n",
    "        pass\n",
    "    # 生成表格--------------\n",
    "    # 训练集&测试集-------\n",
    "    # 等高分布-----\n",
    "    # 训练集\n",
    "    df = score_train[score_train[y] != grey]\n",
    "    df.loc[df.score < score_range[0], \"score\"] = score_range[0]\n",
    "    df.loc[df.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "    df[\"score\"] = pd.cut(\n",
    "        df.score, bins=range(score_range[0], score_range[1] + 1, tick), right=False\n",
    "    )\n",
    "    df = df.groupby(by=[\"score\", y]).size().unstack(level=[1], fill_value=0)\n",
    "    df = df.fillna(0)\n",
    "    df.sort_index(ascending=True, inplace=True)\n",
    "    df[\"训练样本量\"] = df[1] + df[0]\n",
    "    df[\"训练集占比\"] = df[\"训练样本量\"] / df[\"训练样本量\"].sum()\n",
    "    df[\"训练坏客户数\"] = df[1]\n",
    "    df[\"训练坏客户占比\"] = df[\"训练坏客户数\"] / df[\"训练坏客户数\"].sum()\n",
    "    del df[0], df[1]\n",
    "    # 测试集\n",
    "    df1 = score_test[score_test[y] != grey]\n",
    "    df1.loc[df1.score < score_range[0], \"score\"] = score_range[0]\n",
    "    df1.loc[df1.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "    df1[\"score\"] = pd.cut(\n",
    "        df1.score, bins=range(score_range[0], score_range[1] + 1, tick), right=False\n",
    "    )\n",
    "    df1 = df1.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "    df1 = df1.fillna(0)\n",
    "    df1.sort_index(ascending=True, inplace=True)\n",
    "    df1[\"测试样本量\"] = df1[1] + df1[0]\n",
    "    df1[\"测试集占比\"] = df1[\"测试样本量\"] / df1[\"测试样本量\"].sum()\n",
    "    df1[\"测试坏客户数\"] = df1[1]\n",
    "    df1[\"测试坏客户占比\"] = df1[\"测试坏客户数\"] / df1[\"测试坏客户数\"].sum()\n",
    "    del df1[0], df1[1]\n",
    "    # 并表并计算psi\n",
    "    sheet7_1 = df.merge(df1, how=\"outer\", on=\"score\")\n",
    "    sheet7_1 = sheet7_1.fillna(0)\n",
    "    sheet7_1[\"psi\"] = (sheet7_1[\"训练集占比\"] - sheet7_1[\"测试集占比\"]) * np.log(\n",
    "        sheet7_1[\"训练集占比\"] / sheet7_1[\"测试集占比\"]\n",
    "    )\n",
    "    sheet7_1[\"psi_bad\"] = (sheet7_1[\"训练坏客户占比\"] - sheet7_1[\"测试坏客户占比\"]) * np.log(\n",
    "        sheet7_1[\"训练坏客户占比\"] / sheet7_1[\"测试坏客户占比\"]\n",
    "    )\n",
    "\n",
    "    # 等频分布------\n",
    "    # 训练集\n",
    "    dt = score_train[score_train[y] != grey]\n",
    "    dt.loc[dt.score < score_range[0], \"score\"] = score_range[0]\n",
    "    dt.loc[dt.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "    dt.sort_values(by=\"score\", ascending=True, inplace=True)\n",
    "    percent_list = list(range(0, 100 + percent, percent))\n",
    "    breaks = np.percentile(dt.score.values, percent_list)\n",
    "    breaks = list(set(breaks))\n",
    "    breaks = sorted(breaks)\n",
    "    breaks[-1] = score_range[1]\n",
    "    breaks[0] = score_range[0]\n",
    "    dt[\"score\"] = pd.cut(dt.score, bins=breaks, right=False)\n",
    "    dt = dt.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "    dt = dt.fillna(0)\n",
    "    dt.sort_index(ascending=True, inplace=True)\n",
    "    dt[\"训练样本量\"] = dt[1] + dt[0]\n",
    "    dt[\"训练集占比\"] = dt[\"训练样本量\"] / dt[\"训练样本量\"].sum()\n",
    "    dt[\"训练坏客户数\"] = dt[1]\n",
    "    dt[\"训练坏客户占比\"] = dt[\"训练坏客户数\"] / dt[\"训练坏客户数\"].sum()\n",
    "    del dt[0], dt[1]\n",
    "    # 测试集\n",
    "    df1 = score_test[score_test[y] != grey]\n",
    "    df1.loc[df1.score < score_range[0], \"score\"] = score_range[0]\n",
    "    df1.loc[df1.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "    df1.sort_values(by=\"score\", ascending=True, inplace=True)\n",
    "    df1[\"score\"] = pd.cut(df1.score, bins=breaks, right=False)\n",
    "    df1 = df1.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "    df1 = df1.fillna(0)\n",
    "    df1.sort_index(ascending=True, inplace=True)\n",
    "    df1[\"测试样本量\"] = df1[1] + df1[0]\n",
    "    df1[\"测试集占比\"] = df1[\"测试样本量\"] / df1[\"测试样本量\"].sum()\n",
    "    df1[\"测试坏客户数\"] = df1[1]\n",
    "    df1[\"测试坏客户占比\"] = df1[\"测试坏客户数\"] / df1[\"测试坏客户数\"].sum()\n",
    "    del df1[0], df1[1]\n",
    "    # 并表并计算psi\n",
    "    sheet7_2 = dt.merge(df1, how=\"outer\", on=\"score\")\n",
    "    sheet7_2 = sheet7_2.fillna(0)\n",
    "    sheet7_2[\"psi\"] = (sheet7_2[\"训练集占比\"] - sheet7_2[\"测试集占比\"]) * np.log(\n",
    "        sheet7_2[\"训练集占比\"] / sheet7_2[\"测试集占比\"]\n",
    "    )\n",
    "    sheet7_2[\"psi_bad\"] = (sheet7_2[\"训练坏客户占比\"] - sheet7_2[\"测试坏客户占比\"]) * np.log(\n",
    "        sheet7_2[\"训练坏客户占比\"] / sheet7_2[\"测试坏客户占比\"]\n",
    "    )\n",
    "\n",
    "    sheet7_1.to_excel(table, sheet_name=\"7.模型稳定性评估\", startrow=4)\n",
    "    sheet7_2.to_excel(table, sheet_name=\"7.模型稳定性评估\", startrow=4, startcol=12)\n",
    "    row_number = max(sheet7_2.shape[0], sheet7_1.shape[0]) + 4 + 20 + 2\n",
    "\n",
    "    # 有验证集情况\n",
    "    try:\n",
    "        # 等高\n",
    "        df2 = score_oot[score_oot[y] != grey]\n",
    "        df2.loc[df2.score < score_range[0], \"score\"] = score_range[0]\n",
    "        df2.loc[df2.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "        df2[\"score\"] = pd.cut(\n",
    "            df2.score, bins=range(score_range[0], score_range[1] + 1, tick), right=False\n",
    "        )\n",
    "        df2 = df2.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "        df2 = df2.fillna(0)\n",
    "        df2.sort_index(ascending=True, inplace=True)\n",
    "        df2[\"验证样本量\"] = df2[1] + df2[0]\n",
    "        df2[\"验证集占比\"] = df2[\"验证样本量\"] / df2[\"验证样本量\"].sum()\n",
    "        df2[\"验证坏客户数\"] = df2[1]\n",
    "        df2[\"验证坏客户占比\"] = df2[\"验证坏客户数\"] / df2[\"验证坏客户数\"].sum()\n",
    "        del df2[0], df2[1]\n",
    "        # 并表并计算psi\n",
    "        sheet7_1 = df.merge(df2, how=\"outer\", on=\"score\")\n",
    "        sheet7_1 = sheet7_1.fillna(0)\n",
    "        sheet7_1[\"psi\"] = (sheet7_1[\"训练集占比\"] - sheet7_1[\"验证集占比\"]) * np.log(\n",
    "            sheet7_1[\"训练集占比\"] / sheet7_1[\"验证集占比\"]\n",
    "        )\n",
    "        sheet7_1[\"psi_bad\"] = (sheet7_1[\"验证坏客户占比\"] - sheet7_1[\"验证坏客户占比\"]) * np.log(\n",
    "            sheet7_1[\"验证坏客户占比\"] / sheet7_1[\"验证坏客户占比\"]\n",
    "        )\n",
    "        # 等频\n",
    "        df2 = score_oot[score_oot[y] != grey]\n",
    "        df2.loc[df2.score < score_range[0], \"score\"] = score_range[0]\n",
    "        df2.loc[df2.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "        df2[\"score\"] = pd.cut(df2.score, bins=breaks, right=False)\n",
    "        df2 = df2.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "        df2 = df2.fillna(0)\n",
    "        df2.sort_index(ascending=True, inplace=True)\n",
    "        df2[\"验证样本量\"] = df2[1] + df2[0]\n",
    "        df2[\"验证集占比\"] = df2[\"验证样本量\"] / df2[\"验证样本量\"].sum()\n",
    "        df2[\"验证坏客户数\"] = df2[1]\n",
    "        df2[\"验证坏客户占比\"] = df2[\"验证坏客户数\"] / df2[\"验证坏客户数\"].sum()\n",
    "        del df2[0], df2[1]\n",
    "        # 并表并计算psi\n",
    "        sheet7_2 = dt.merge(df2, how=\"outer\", on=\"score\")\n",
    "        sheet7_2 = sheet7_2.fillna(0)\n",
    "        sheet7_2[\"psi\"] = (sheet7_2[\"训练集占比\"] - sheet7_2[\"验证集占比\"]) * np.log(\n",
    "            sheet7_2[\"训练集占比\"] / sheet7_2[\"验证集占比\"]\n",
    "        )\n",
    "        sheet7_2[\"psi_bad\"] = (sheet7_2[\"训练坏客户占比\"] - sheet7_2[\"验证坏客户占比\"]) * np.log(\n",
    "            sheet7_2[\"训练坏客户占比\"] / sheet7_2[\"验证坏客户占比\"]\n",
    "        )\n",
    "        title1 = pd.DataFrame(columns=[\"2.训练&验证\"])\n",
    "        title1.to_excel(\n",
    "            table, sheet_name=\"7.模型稳定性评估\", index=False, startrow=row_number\n",
    "        )\n",
    "        title2_1.to_excel(\n",
    "            table, sheet_name=\"7.模型稳定性评估\", index=False, startrow=row_number + 1\n",
    "        )\n",
    "        title2_2.to_excel(\n",
    "            table,\n",
    "            sheet_name=\"7.模型稳定性评估\",\n",
    "            index=False,\n",
    "            startrow=row_number + 1,\n",
    "            startcol=12,\n",
    "        )\n",
    "\n",
    "        sheet7_1.to_excel(table, sheet_name=\"7.模型稳定性评估\", startrow=row_number + 3)\n",
    "        sheet7_2.to_excel(\n",
    "            table, sheet_name=\"7.模型稳定性评估\", startrow=row_number + 3, startcol=12\n",
    "        )\n",
    "    except:\n",
    "        pass\n",
    "    # -------------------------------------------------------------------------------------------------------------------------\n",
    "    # 8.变量重要性\n",
    "    feature_imp = pd.Series(model.feature_importances_)\n",
    "    feature_name = pd.Series(model.booster_.feature_name())\n",
    "    feature_df = pd.DataFrame({'feature_name': feature_name, 'element': feature_imp})\n",
    "    feature_df.sort_values('element', ascending=False, inplace=True)\n",
    "    feature_df.set_index('feature_name', drop=True, inplace=True)\n",
    "    try:\n",
    "        iv = sc.iv(data_total[model.booster_.feature_name()+['flagy']],'flagy')\n",
    "        missing_rate = data_total[model.booster_.feature_name()].apply(lambda x: x.isna().sum() / x.shape[0])\n",
    "        iv.set_index('variable', drop=True, inplace=True)\n",
    "        missing_rate = pd.DataFrame(missing_rate, columns=['缺失值占比'])\n",
    "\n",
    "        sheet_8 = pd.concat([feature_df, iv, missing_rate], axis=1)\n",
    "        sheet_8 = sheet_8.reset_index()\n",
    "        sheet_8.columns = ['变量','重要性','IV值','缺失值占比']\n",
    "        sheet_8[\"序号\"] = list(range(1, sheet_8.shape[0] + 1))\n",
    "        sheet_8[\"解释\"] = \"\"\n",
    "        sheet_8 = sheet_8[[\"序号\", \"变量\", \"解释\", \"重要性\", \"IV值\", \"缺失值占比\"]]\n",
    "    except:\n",
    "        sheet_8 = pd.DataFrame(columns=[\"序号\", \"变量\", \"解释\", \"重要性\", \"IV值\", \"缺失值占比\"])\n",
    "\n",
    "    head.to_excel(table, sheet_name=\"8.变量重要性\", index=False)\n",
    "    title.to_excel(table, sheet_name=\"8.变量重要性\", index=False, startrow=1)\n",
    "    sheet_8.to_excel(table, sheet_name=\"8.变量重要性\", index=False, startrow=2)\n",
    "    # -------------------------------------------------------------------------------------------------------------------------\n",
    "    # 9.变量趋势\n",
    "    import matplotlib.pyplot as plt\n",
    "    title = pd.DataFrame(columns = [\"重要变量风险表现\"])\n",
    "    var = feature_df.index.tolist()[:top_n]\n",
    "    data_bins = sc.woebin(data_total[var + [y]],y)\n",
    "    sheet_9 = pd.concat(data_bins, ignore_index=True)[[\"variable\", \"bin\",\"count\", \"count_distr\", \"bad\", \"badprob\"]]\n",
    "    title1 = pd.DataFrame(columns = [\"序号\",\"变量\",\"分箱\",\"区间数量\",\"区间占比\",\"区间坏客数\",\"区间坏客率\"])\n",
    "    head.to_excel(table, sheet_name=\"9.变量趋势\", index=False)\n",
    "    title.to_excel(table, sheet_name=\"9.变量趋势\", index=False, startrow=1)\n",
    "    title1.to_excel(table, sheet_name=\"9.变量趋势\", index=False, startrow=2)\n",
    "    sheet_9.to_excel(\n",
    "        table, sheet_name=\"9.变量趋势\", index=False, startrow=3, startcol=1, header=False\n",
    "    )\n",
    "    sheet = table.book.sheetnames[\"9.变量趋势\"]\n",
    "    \n",
    "    bin_pict = sc.woebin_plot(data_bins)\n",
    "    i = 3\n",
    "    for pict in list(sheet_9.variable.unique()):\n",
    "        bin_pict[pict].savefig(pict + \".png\", bbox_inches=\"tight\")\n",
    "        sheet.insert_image(\n",
    "            \"J\" + str(i), pict + \".png\", {\"x_scale\": 0.6, \"y_scale\": 0.6}\n",
    "        )\n",
    "        i = i + 12\n",
    "    # -------------------------------------------------------------------------------------------------------------------------\n",
    "    # 10.样本风险评分分布\n",
    "    title1 = pd.DataFrame(columns=[\"1、等高分布\"])\n",
    "    title2 = pd.DataFrame(columns=[\"分数整体分布情况-训练集\"])\n",
    "    title3 = pd.DataFrame(columns=[\"分数整体分布情况-测试集\"])\n",
    "    title4 = pd.DataFrame(columns=[\"分数整体分布情况-验证集\"])\n",
    "    title5 = pd.DataFrame(columns=[\"分数整体分布情况-训练集+测试集\"])\n",
    "    head.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False)\n",
    "    title1.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=1)\n",
    "    title2.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=2)\n",
    "    title3.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=19)\n",
    "    title5.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=2,startcol=11)\n",
    "    title5.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=55,startcol=11)\n",
    "    # 等高---------\n",
    "    # 训练+测试\n",
    "    score_total = pd.concat([score_train,score_test],axis=0,sort=False)\n",
    "    df = score_total[score_total[y] != grey]\n",
    "    df.loc[df.score < score_range[0], \"score\"] = score_range[0]\n",
    "    df.loc[df.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "    df[\"score\"] = pd.cut(df.score, bins=range(score_range[0], score_range[1] + 1, tick),right=False)\n",
    "    df = df.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "    df = df.fillna(0)\n",
    "    df[\"区间人数\"] = df[0] + df[1]\n",
    "    df[\"区间占比\"] = df[\"区间人数\"] / df[\"区间人数\"].sum()\n",
    "    df[\"区间坏客户率\"] = df[1] / df[\"区间人数\"]\n",
    "    df[\"累计坏客户占比\"] = df[1].cumsum() / df[1].sum()\n",
    "    df[\"累计好客户占比\"] = df[0].cumsum() / df[0].sum()\n",
    "    df[\"好坏区分程度(ks)\"] = df[\"累计坏客户占比\"] - df[\"累计好客户占比\"]\n",
    "    df.reset_index(inplace=True)\n",
    "    df.rename(columns={\"score\": \"评分区间\"}, inplace=True)\n",
    "    del df[0], df[1]\n",
    "    df.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=3,startcol = 11)\n",
    "    # 训练集\n",
    "    df = score_train[score_train[y] != grey]\n",
    "    df.loc[df.score < score_range[0], \"score\"] = score_range[0]\n",
    "    df.loc[df.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "    df[\"score\"] = pd.cut(\n",
    "        df.score, bins=range(score_range[0], score_range[1] + 1, tick), right=False\n",
    "    )\n",
    "    df = df.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "    df = df.fillna(0)\n",
    "    df[\"区间人数\"] = df[0] + df[1]\n",
    "    df[\"区间占比\"] = df[\"区间人数\"] / df[\"区间人数\"].sum()\n",
    "    df[\"区间坏客户率\"] = df[1] / df[\"区间人数\"]\n",
    "    df[\"累计坏客户占比\"] = df[1].cumsum() / df[1].sum()\n",
    "    df[\"累计好客户占比\"] = df[0].cumsum() / df[0].sum()\n",
    "    df[\"好坏区分程度(ks)\"] = df[\"累计坏客户占比\"] - df[\"累计好客户占比\"]\n",
    "    df.reset_index(inplace=True)\n",
    "    df.rename(columns={\"score\": \"评分区间\"}, inplace=True)\n",
    "    del df[0], df[1]\n",
    "    df.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=3)\n",
    "    # 测试集\n",
    "    df = score_test[score_test[y] != grey]\n",
    "    df.loc[df.score < score_range[0], \"score\"] = score_range[0]\n",
    "    df.loc[df.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "    df[\"score\"] = pd.cut(\n",
    "        df.score, bins=range(score_range[0], score_range[1] + 1, tick), right=False\n",
    "    )\n",
    "    df = df.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "    df = df.fillna(0)\n",
    "    df[\"区间人数\"] = df[0] + df[1]\n",
    "    df[\"区间占比\"] = df[\"区间人数\"] / df[\"区间人数\"].sum()\n",
    "    df[\"区间坏客户率\"] = df[1] / df[\"区间人数\"]\n",
    "    df[\"累计坏客户占比\"] = df[1].cumsum() / df[1].sum()\n",
    "    df[\"累计好客户占比\"] = df[0].cumsum() / df[0].sum()\n",
    "    df[\"好坏区分程度(ks)\"] = df[\"累计坏客户占比\"] - df[\"累计好客户占比\"]\n",
    "    df.reset_index(inplace=True)\n",
    "    df.rename(columns={\"score\": \"评分区间\"}, inplace=True)\n",
    "    del df[0], df[1]\n",
    "    df.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=20)\n",
    "    # 等频---------\n",
    "    title1 = pd.DataFrame(columns=[\"1、等频分布\"])\n",
    "    title1.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=54)\n",
    "    title2.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=55)\n",
    "    title3.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=77)\n",
    "    # 训练+测试\n",
    "    score_total = pd.concat([score_train,score_test],axis=0,sort=False)\n",
    "    df = score_total[score_total[y] != grey]\n",
    "    df.sort_values(by=\"score\", ascending=True, inplace=True)\n",
    "    df.loc[df.score < score_range[0], \"score\"] = score_range[0]\n",
    "    df.loc[df.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "    percent_list = list(range(0, 100 + percent, percent))\n",
    "    breaks = np.percentile(df.score.values, percent_list)\n",
    "    breaks = list(set(breaks))\n",
    "    breaks = sorted(breaks)\n",
    "    breaks[-1] = score_range[1]\n",
    "    breaks[0] = score_range[0]\n",
    "    df[\"score\"] = pd.cut(df.score, bins=breaks, right=False)\n",
    "    df = df.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "    df = df.fillna(0)\n",
    "    df[\"区间人数\"] = df[0] + df[1]\n",
    "    df[\"区间占比\"] = df[\"区间人数\"] / df[\"区间人数\"].sum()\n",
    "    df[\"区间坏客户率\"] = df[1] / df[\"区间人数\"]\n",
    "    df[\"累计坏客户占比\"] = df[1].cumsum() / df[1].sum()\n",
    "    df[\"累计好客户占比\"] = df[0].cumsum() / df[0].sum()\n",
    "    df[\"好坏区分程度(ks)\"] = df[\"累计坏客户占比\"] - df[\"累计好客户占比\"]\n",
    "    df.reset_index(inplace=True)\n",
    "    df.rename(columns={\"score\": \"评分区间\"}, inplace=True)\n",
    "    del df[0], df[1]\n",
    "    df.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=56,startcol = 11)\n",
    "    # 训练集\n",
    "    dt = score_train[score_train[y] != grey]\n",
    "    dt.sort_values(by=\"score\", ascending=True, inplace=True)\n",
    "    dt.loc[dt.score < score_range[0], \"score\"] = score_range[0]\n",
    "    dt.loc[dt.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "    dt[\"score\"] = pd.cut(dt.score, bins=breaks, right=False)\n",
    "    dt = dt.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "    dt = dt.fillna(0)\n",
    "    dt[\"区间人数\"] = dt[0] + dt[1]\n",
    "    dt[\"区间占比\"] = dt[\"区间人数\"] / dt[\"区间人数\"].sum()\n",
    "    dt[\"区间坏客户率\"] = dt[1] / dt[\"区间人数\"]\n",
    "    dt[\"累计坏客户占比\"] = dt[1].cumsum() / dt[1].sum()\n",
    "    dt[\"累计好客户占比\"] = dt[0].cumsum() / dt[0].sum()\n",
    "    dt[\"好坏区分程度(ks)\"] = dt[\"累计坏客户占比\"] - dt[\"累计好客户占比\"]\n",
    "    dt.reset_index(inplace=True)\n",
    "    dt.rename(columns={\"score\": \"评分区间\"}, inplace=True)\n",
    "    del dt[0], dt[1]\n",
    "    dt.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=56)\n",
    "    # 测试集\n",
    "    dt = score_test[score_test[y] != grey]\n",
    "    dt.sort_values(by=\"score\", ascending=True, inplace=True)\n",
    "    dt.loc[dt.score < score_range[0], \"score\"] = score_range[0]\n",
    "    dt.loc[dt.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "    dt[\"score\"] = pd.cut(dt.score, bins=breaks, right=False)\n",
    "    dt = dt.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "    dt = dt.fillna(0)\n",
    "    dt[\"区间人数\"] = dt[0] + dt[1]\n",
    "    dt[\"区间占比\"] = dt[\"区间人数\"] / dt[\"区间人数\"].sum()\n",
    "    dt[\"区间坏客户率\"] = dt[1] / dt[\"区间人数\"]\n",
    "    dt[\"累计坏客户占比\"] = dt[1].cumsum() / dt[1].sum()\n",
    "    dt[\"累计好客户占比\"] = dt[0].cumsum() / dt[0].sum()\n",
    "    dt[\"好坏区分程度(ks)\"] = dt[\"累计坏客户占比\"] - dt[\"累计好客户占比\"]\n",
    "    dt.reset_index(inplace=True)\n",
    "    dt.rename(columns={\"score\": \"评分区间\"}, inplace=True)\n",
    "    del dt[0], dt[1]\n",
    "    dt.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=78)    \n",
    "    \n",
    "    # 有验证集情况\n",
    "    try:\n",
    "        # 等高\n",
    "        title4.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=37)\n",
    "        df = score_oot[score_oot[y] != grey]\n",
    "        df.loc[df.score < score_range[0], \"score\"] = score_range[0]\n",
    "        df.loc[df.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "        df[\"score\"] = pd.cut(\n",
    "            df.score, bins=range(score_range[0], score_range[1] + 1, tick), right=False\n",
    "        )\n",
    "        df = df.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "        df = df.fillna(0)\n",
    "        df[\"区间人数\"] = df[0] + df[1]\n",
    "        df[\"区间占比\"] = df[\"区间人数\"] / df[\"区间人数\"].sum()\n",
    "        df[\"区间坏客户率\"] = df[1] / df[\"区间人数\"]\n",
    "        df[\"累计坏客户占比\"] = df[1].cumsum() / df[1].sum()\n",
    "        df[\"累计好客户占比\"] = df[0].cumsum() / df[0].sum()\n",
    "        df[\"好坏区分程度(ks)\"] = df[\"累计坏客户占比\"] - df[\"累计好客户占比\"]\n",
    "        df.reset_index(inplace=True)\n",
    "        df.rename(columns={\"score\": \"评分区间\"}, inplace=True)\n",
    "        del df[0], df[1]\n",
    "        df.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=38)\n",
    "        # 等频\n",
    "        title4.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=104)\n",
    "        dt = score_oot[score_oot[y] != grey]\n",
    "        dt.sort_values(by=\"score\", ascending=True, inplace=True)\n",
    "        dt.loc[dt.score < score_range[0], \"score\"] = score_range[0]\n",
    "        dt.loc[dt.score >= score_range[1], \"score\"] = score_range[1] - 1\n",
    "        dt[\"score\"] = pd.cut(dt.score, bins=breaks, right=False)\n",
    "        dt = dt.groupby(by=[\"score\", \"flagy\"]).size().unstack(level=[1], fill_value=0)\n",
    "        dt = dt.fillna(0)\n",
    "        dt[\"区间人数\"] = dt[0] + dt[1]\n",
    "        dt[\"区间占比\"] = dt[\"区间人数\"] / dt[\"区间人数\"].sum()\n",
    "        dt[\"区间坏客户率\"] = dt[1] / dt[\"区间人数\"]\n",
    "        dt[\"累计坏客户占比\"] = dt[1].cumsum() / dt[1].sum()\n",
    "        dt[\"累计好客户占比\"] = dt[0].cumsum() / dt[0].sum()\n",
    "        dt[\"好坏区分程度(ks)\"] = dt[\"累计坏客户占比\"] - dt[\"累计好客户占比\"]\n",
    "        dt.reset_index(inplace=True)\n",
    "        dt.rename(columns={\"score\": \"评分区间\"}, inplace=True)\n",
    "        del dt[0], dt[1]\n",
    "        dt.to_excel(table, sheet_name=\"10.样本风险评分分布\", index=False, startrow=105)\n",
    "    except:\n",
    "        pass\n",
    "    # -------------------------------------------------------------------------------------------------------------------------\n",
    "    # 11.评分决策表\n",
    "    head.to_excel(table, sheet_name=\"11.评分决策表\", index=False)\n",
    "    title1 = pd.DataFrame(columns=[\"1、等高\"])\n",
    "    title2 = pd.DataFrame(columns=[\"2、等频\"])\n",
    "    title3 = pd.DataFrame(columns=[\"评分决策表\"])\n",
    "    # 等高\n",
    "    title1.to_excel(table, sheet_name=\"11.评分决策表\", index=False, startrow=1)\n",
    "    title3.to_excel(table, sheet_name=\"11.评分决策表\", index=False, startrow=2)\n",
    "    \n",
    "    total_class_pred = model.predict_proba(data_total[var_final])[:,1]\n",
    "    score_total = np.around(A - B * np.log(total_class_pred/(1 - total_class_pred)))\n",
    "    score_total = pd.DataFrame(score_total,index = data_total.index)\n",
    "    score_total = pd.concat([data_total['flagy'],score_total],axis = 1).rename(columns = {0:'score'})\n",
    "    # score_total = pd.concat([score_train, score_test], axis=0, sort=False)\n",
    "\n",
    "    score_total.loc[score_total.score < score_range[0], \"score\"] = score_range[0]\n",
    "    score_total.loc[score_total.score >= score_range[1], \"score\"] = score_range[1] - 1 \n",
    "    score_total[\"score\"] = pd.cut(\n",
    "        score_total.score,\n",
    "        bins=range(score_range[0], score_range[1] + 1, tick),\n",
    "        right=False,\n",
    "    )\n",
    "    score_total = (\n",
    "        score_total.groupby(by=[\"score\", \"flagy\"])\n",
    "        .size()\n",
    "        .unstack(level=[1], fill_value=0)\n",
    "    )\n",
    "    score_total = score_total.fillna(0)\n",
    "    score_total.sort_index(ascending=False, inplace=True)\n",
    "    score_total[\"好\"] = score_total[0]\n",
    "    try:\n",
    "        score_total[\"灰\"] = score_total[grey]\n",
    "    except:\n",
    "        print(\"Warning: No Grey sample!\")\n",
    "    score_total[\"坏\"] = score_total[1]\n",
    "    try:\n",
    "        score_total[\"总\"] = score_total[0] + score_total[1] + score_total[grey]\n",
    "    except:\n",
    "        score_total[\"总\"] = score_total[0] + score_total[1]\n",
    "    score_total[\"坏累计\"] = score_total[1].cumsum()\n",
    "    score_total[\"总累计\"] = score_total[\"总\"].cumsum()\n",
    "    score_total[\"通过率\"] = score_total[\"总累计\"] / score_total[\"总\"].sum()\n",
    "    score_total[\"每段违约率\"] = score_total[\"坏\"] / score_total[\"总\"]\n",
    "    score_total[\"平均违约率\"] = score_total[\"坏\"].sum() / score_total[\"总\"].sum()\n",
    "    score_total[\"通过违约率\"] = score_total[\"坏累计\"] / score_total[\"总累计\"]\n",
    "    score_total[\"拒绝违约率\"] = (score_total[\"坏\"].sum() - score_total[\"坏累计\"]) / (\n",
    "        score_total[\"总\"].sum() - score_total[\"总累计\"]\n",
    "    )\n",
    "    score_total[\"违约率下降\"] = 1 - score_total[\"通过违约率\"] / score_total[\"平均违约率\"]\n",
    "    score_total.reset_index(inplace=True)\n",
    "    score_total.rename(columns={\"score\": \"评分区间\"}, inplace=True)\n",
    "    del score_total[1], score_total[0]\n",
    "    score_total.to_excel(table, sheet_name=\"11.评分决策表\", index=False, startrow=3)\n",
    "    # 等频\n",
    "    title2.to_excel(table, sheet_name=\"11.评分决策表\", index=False, startrow=25)\n",
    "    title3.to_excel(table, sheet_name=\"11.评分决策表\", index=False, startrow=26)\n",
    "    \n",
    "    total_class_pred = model.predict_proba(data_total[var_final])[:,1]\n",
    "    score_total = np.around(A - B * np.log(total_class_pred/(1 - total_class_pred)))\n",
    "    score_total = pd.DataFrame(score_total,index = data_total.index)\n",
    "    score_total = pd.concat([data_total['flagy'],score_total],axis = 1).rename(columns = {0:'score'})\n",
    "    #score_total = pd.concat([score_train, score_test], axis=0, sort=False)\n",
    "\n",
    "    score_total.loc[score_total.score < score_range[0], \"score\"] = score_range[0]\n",
    "    score_total.loc[score_total.score >= score_range[1], \"score\"] = score_range[1]\n",
    "    percent_list = list(range(0, 100 + percent, percent))\n",
    "    breaks = np.percentile(score_total.score.values, percent_list)\n",
    "    breaks = list(set(breaks))\n",
    "    breaks = sorted(breaks)\n",
    "    breaks[-1] = score_range[1]\n",
    "    breaks[0] = score_range[0]\n",
    "    score_total[\"score\"] = pd.cut(score_total.score, bins=breaks, right=False)\n",
    "    score_total = (\n",
    "        score_total.groupby(by=[\"score\", \"flagy\"])\n",
    "        .size()\n",
    "        .unstack(level=[1], fill_value=0)\n",
    "    )\n",
    "    score_total = score_total.fillna(0)\n",
    "    score_total.sort_index(ascending=False, inplace=True)\n",
    "    score_total[\"好\"] = score_total[0]\n",
    "    try:\n",
    "        score_total[\"灰\"] = score_total[grey]\n",
    "    except:\n",
    "        print(\"Warning: No Grey sample!\")\n",
    "    score_total[\"坏\"] = score_total[1]\n",
    "    try:\n",
    "        score_total[\"总\"] = score_total[0] + score_total[1] + score_total[grey]\n",
    "    except:\n",
    "        score_total[\"总\"] = score_total[0] + score_total[1]\n",
    "    score_total[\"坏累计\"] = score_total[1].cumsum()\n",
    "    score_total[\"总累计\"] = score_total[\"总\"].cumsum()\n",
    "    score_total[\"通过率\"] = score_total[\"总累计\"] / score_total[\"总\"].sum()\n",
    "    score_total[\"每段违约率\"] = score_total[\"坏\"] / score_total[\"总\"]\n",
    "    score_total[\"平均违约率\"] = score_total[\"坏\"].sum() / score_total[\"总\"].sum()\n",
    "    score_total[\"通过违约率\"] = score_total[\"坏累计\"] / score_total[\"总累计\"]\n",
    "    score_total[\"拒绝违约率\"] = (score_total[\"坏\"].sum() - score_total[\"坏累计\"]) / (\n",
    "        score_total[\"总\"].sum() - score_total[\"总累计\"]\n",
    "    )\n",
    "    score_total[\"违约率下降\"] = 1 - score_total[\"通过违约率\"] / score_total[\"平均违约率\"]\n",
    "    score_total.reset_index(inplace=True)\n",
    "    score_total.rename(columns={\"score\": \"评分区间\"}, inplace=True)\n",
    "    del score_total[1], score_total[0]\n",
    "    score_total.to_excel(table, sheet_name=\"11.评分决策表\", index=False, startrow=27)\n",
    "    table.save()    \n",
    "    \n",
    "    return 1"
   ]
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